CN108932510A - A kind of rubbish detection method and device - Google Patents
A kind of rubbish detection method and device Download PDFInfo
- Publication number
- CN108932510A CN108932510A CN201810951193.7A CN201810951193A CN108932510A CN 108932510 A CN108932510 A CN 108932510A CN 201810951193 A CN201810951193 A CN 201810951193A CN 108932510 A CN108932510 A CN 108932510A
- Authority
- CN
- China
- Prior art keywords
- rubbish
- image
- detected
- detection method
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000010813 municipal solid waste Substances 0.000 title claims abstract description 285
- 238000001514 detection method Methods 0.000 title claims abstract description 156
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 47
- 239000013598 vector Substances 0.000 claims abstract description 25
- 238000000605 extraction Methods 0.000 claims abstract description 20
- 238000012549 training Methods 0.000 claims description 70
- 238000000034 method Methods 0.000 claims description 22
- 238000012545 processing Methods 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 abstract description 7
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 10
- 230000008569 process Effects 0.000 description 9
- 230000015572 biosynthetic process Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 230000002093 peripheral effect Effects 0.000 description 5
- 238000007689 inspection Methods 0.000 description 4
- 238000003786 synthesis reaction Methods 0.000 description 4
- 239000002699 waste material Substances 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 208000014674 injury Diseases 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- SGHZXLIDFTYFHQ-UHFFFAOYSA-L Brilliant Blue Chemical compound [Na+].[Na+].C=1C=C(C(=C2C=CC(C=C2)=[N+](CC)CC=2C=C(C=CC=2)S([O-])(=O)=O)C=2C(=CC=CC=2)S([O-])(=O)=O)C=CC=1N(CC)CC1=CC=CC(S([O-])(=O)=O)=C1 SGHZXLIDFTYFHQ-UHFFFAOYSA-L 0.000 description 1
- 208000033748 Device issues Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Image Analysis (AREA)
Abstract
The present invention provides a kind of rubbish detection method and device, are related to image procossing and technical field of environmental detection.The rubbish detection method acquires the image to be detected of first area in situation to be detected first, feature extraction is carried out to benchmark image of the first area in no rubbish and obtains first eigenvector, feature extraction is carried out to described image to be detected and obtains second feature vector, determine that the similarity of the first eigenvector and the second feature vector is greater than default similar threshold value, then judged in described image to be detected using convolutional neural networks object detection model with the presence or absence of rubbish image, if so, determining that there are rubbish for the first area.The rubbish detection method carries out automatic detection, identification and the alarm of rubbish by image procossing and neural network model, improves the efficiency and accuracy of rubbish detection, reduces cost of labor.
Description
Technical field
The present invention relates to image procossing and technical field of environmental detection, in particular to a kind of rubbish detection method and
Device.
Background technique
With the quickening of ur- banization speed and the sustainable growth of population size, the scale of cell is gradually expanded, and cell enters
Firmly rate also improves rapidly, and population density's increase then brings huge challenge to cell environment.It safeguards that cell is clean and tidy, keeps
Graceful environment becomes particularly important.Cell environment, which becomes the comprehensive of cell overall image, to be embodied, and the life of resident is directly affected
Quality and health living.And cell rubbish be influence cell environment a key element, cell road surface rubbish monitoring with
Tidiness evaluation becomes the important content of cell regulatory authorities routine work.
Cell pavement garbage monitoring and evaluation is manually patrolled and registration of taking pictures mainly by special messenger at present.This artificial detection
Method needs to expend a large amount of manpower and material resources, and is had a holiday by weather, personnel and influenced with the various extraneous factors such as working time,
There is a problem of that low efficiency, there may be mistake registration, human cost are high.
Summary of the invention
In view of this, the embodiment of the present invention is designed to provide a kind of rubbish detection method and device, it is above-mentioned to solve
Low efficiency present in existing artificial rubbish detection method, there may be the high problems of mistake registration, human cost.
In a first aspect, the rubbish detection method acquires first the embodiment of the invention provides a kind of rubbish detection method
The image to be detected of first area in situation to be detected carries out benchmark image of the first area in no rubbish
Feature extraction obtains first eigenvector, carries out feature extraction to described image to be detected and obtains second feature vector, determines institute
The similarity for stating first eigenvector and the second feature vector is less than default similar threshold value, using convolutional neural networks object
Detection model judges with the presence or absence of rubbish image in described image to be detected, if so, determining that there are rubbish for the first area.
It is comprehensive in a first aspect, in the case that it is described to the first area in no rubbish benchmark image carry out feature extraction
Before obtaining first eigenvector, the rubbish detection method further include: acquire the first area in no rubbish
Benchmark image.
It is comprehensive in a first aspect, in the acquisition first area after image to be detected in situation to be detected, Yi Jisuo
State benchmark image to the first area in no rubbish and carry out feature extraction and obtain first eigenvector, to it is described to
Before detection image carries out feature extraction acquisition second feature vector, the rubbish detection method further include: to the reference map
Picture and described image to be detected carry out size normalization processing.
It is comprehensive in a first aspect, it is described judged using convolutional neural networks object detection model be in described image to be detected
Before the no image there are rubbish, the rubbish detection method further include: acquire several described first areas respectively in no rubbish feelings
Under condition and there is the image in the case of rubbish as training sample；Training set is obtained after carrying out data extending to the training sample；
Convolutional neural networks object detection model training is carried out based on the training set, obtains convolutional neural networks object detection model.
Synthesis is in a first aspect, obtain training set after the progress data extending to the training sample, comprising: to the instruction
Practice sample to carry out mirror image, invert, cut out, obtaining training set after the data extending of color disturbance.
Synthesis is in a first aspect, described carry out convolutional neural networks object detection model training based on the training set, comprising:
The pre- instruction of garbage classification task is carried out to the archetype of convolutional neural networks object detection model on ImageNet data set
Practice, the initial model of the convolutional neural networks object detection model is obtained according to the model parameter value that the pre-training obtains；
Arameter optimization is carried out to the initial model using the training set, obtains convolutional neural networks object detection model.
Synthesis is in a first aspect, the rubbish detection method is also wrapped in the determination first area there are after rubbish
It includes: determining the grade of rubbish alarm according to position, type and the size of rubbish in the rubbish image；To the sending pair of default object
Answer the rubbish alarm of grade.
Synthesis is in a first aspect, determine that rubbish is alert in position, type and the size according to rubbish in the rubbish image
Before the grade of report, the rubbish detection method further include: described image to be detected is divided into multiple subregions, each subregion point
Different rubbish position weight value l is not corresponded to, identifies the subregion of rubbish corresponding position in described image to be detected, is obtained
The corresponding rubbish position weight value l of the subregion；Calculate the ratio s of the rubbish image Yu image to be detected size；It will
Different rubbish types is correspondingly arranged different rubbish class label c, is identified in the rubbish image using depth network model
Rubbish type；The rubbish class parameter θ that the rubbish is obtained by rubbish alert level formula θ=c (l+s), according to described
The affiliated threshold interval of rubbish class parameter θ determines the grade of rubbish alarm.
Second aspect, the embodiment of the invention provides a kind of rubbish detection device, the rubbish detection device includes image
Obtain module, characteristic extracting module, similarity judgment module and rubbish judgment module.Described image obtains module for obtaining the
The image to be detected of one region in situation to be detected.The characteristic extracting module is used for the first area in no rubbish feelings
Benchmark image under condition carries out feature extraction and obtains first eigenvector, carries out feature extraction to described image to be detected and obtains the
Two feature vectors.The similarity judgment module is used to determine the similar of the first eigenvector and the second feature vector
Degree is greater than default similar threshold value.The rubbish judgment module be used for using the judgement of convolutional neural networks object detection model it is described to
It whether there is rubbish image in detection image, if so, further determining that rubbish classification.
Comprehensive second aspect, the rubbish detection device further include that training sample obtains module, training set obtains module and
Convolutional neural networks object detection model training module.The training sample obtain module for obtain respectively several described first
Region is in no rubbish and has the image in the case of rubbish as training sample.The training set obtains module and is used for institute
It states after training sample carries out data extending and obtains training set.The convolutional neural networks object detection model training module is used for base
Convolutional neural networks object detection model training is carried out in the training set, obtains convolutional neural networks object detection model.
Comprehensive second aspect, the rubbish detection device further include position weight value computing module, image size calculating mould
Block, rubbish category identification module and rubbish alert level determining module.The position weight value computing module be used for will it is described to
Detection image is divided into multiple subregions, and each subregion respectively corresponds different rubbish position weight value l, identifies the rubbish in institute
The subregion for stating corresponding position in image to be detected obtains the corresponding rubbish position weight value l of the subregion.The big subtotal of described image
Calculate the ratio s that module is used to calculate the rubbish image Yu image to be detected size.The rubbish category identification module is used
In different rubbish types to be correspondingly arranged to different rubbish class label c, the rubbish image is identified using depth network model
In rubbish type.The rubbish alert level determining module is used to obtain by rubbish alert level formula θ=c (l+s)
The rubbish class parameter θ of the rubbish determines the grade of rubbish alarm according to the affiliated threshold interval of the rubbish class parameter θ.
The third aspect, the embodiment of the invention also provides a kind of storage medium, the storage medium is stored in computer,
The storage medium includes a plurality of instruction, and a plurality of instruction is configured such that the computer executes above-mentioned method.
Beneficial effect provided by the invention is:
The present invention provides a kind of rubbish detection method, the rubbish detection method is using first area in no rubbish situation
Under image carry out image procossing as image to be detected for needing to carry out rubbish detection of benchmark image and subsequent acquisition, be based on
Image procossing and neural network model carry out rubbish detection by computer automatically, improve the efficiency of rubbish detection and accurate
Property, reduce cost of labor.Wherein, the rubbish detection method using neural network model carry out the judgement of rubbish image before,
Also the feature vector similarity of the benchmark image and described image to be detected is compared, it is pre- to determine that the similarity is less than
If just executing the rubbish image judgement when threshold value, avoid directlying adopt neural network model progress rubbish image judgement and wasting
A large amount of computing resource further improves the efficiency and resource utilization of the rubbish detection.Meanwhile using convolutional Neural net
Network object detection model is judged with the presence or absence of rubbish image in described image to be detected, can pass through convolutional neural networks
Frame in object detection model, which is returned, carries out identification and frame choosing to the rubbish image in described image to be detected, improves rubbish
The accuracy of detection.
Other features and advantages of the present invention will be illustrated in subsequent specification, also, partly be become from specification
It is clear that by implementing understanding of the embodiment of the present invention.The objectives and other advantages of the invention can be by written theory
Specifically noted structure is achieved and obtained in bright book, claims and attached drawing.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is a kind of flow diagram for rubbish detection method that first embodiment of the invention provides；
Fig. 2 is a kind of process for convolutional neural networks object detection model training step that first embodiment of the invention provides
Schematic diagram；
Fig. 3 is a kind of flow diagram for rubbish alarm step that first embodiment of the invention provides；
Fig. 4 is a kind of module diagram for rubbish detection device that second embodiment of the invention provides；
Fig. 5 is a kind of structure that can be applied to the electronic equipment in the embodiment of the present application that third embodiment of the invention provides
Block diagram.
Icon: 100- rubbish detection device；110- image collection module；120- characteristic extracting module；130- similarity is sentenced
Disconnected module；140- rubbish judgment module；200- electronic equipment；201- memory；202- storage control；203- processor；
204- Peripheral Interface；205- input-output unit；206- audio unit；207- display unit.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile of the invention
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
First embodiment
Through the applicant the study found that existing rubbish detection method relies primarily on artificial progress, manually patrols and take pictures
Registration.This artificial detection method needs to expend a large amount of manpower and material resources, and had a holiday with the working time by weather, personnel etc.
The influence of various extraneous factors.With domestic and international each big city " smart city ", the especially development of " intelligence community " work, such as
What improves the informationization of cell environment monitoring and evaluation, intelligent level becomes an important problem.To solve the above-mentioned problems,
First embodiment of the invention provides a kind of rubbish detection method, with improve rubbish detection the degree of automation, detection efficiency and
Accuracy.
Referring to FIG. 1, Fig. 1 is a kind of flow diagram for rubbish detection method that first embodiment of the invention provides.
Step S10: it is to be detected in situation to be detected that detection service device controls image acquisition device first area
Image.
Step S20: the detection service device carries out feature to benchmark image of the first area in no rubbish
It extracts and obtains first eigenvector, feature extraction is carried out to described image to be detected and obtains second feature vector.
Step S30: the detection service device determines the similarity of the first eigenvector and the second feature vector
Less than default similar threshold value.
Step S40: the detection service device judges described image to be detected using convolutional neural networks object detection model
In whether there is rubbish image.
Step S50: if so, determining that there are rubbish for the first area.
For step S10, the first area can be designated area, specified community or other specified regions.The inspection
Survey server can be computer, image processing workstations, Cloud Server or other have image procossing and basic operation function
The processing equipment of energy.Described image acquisition device can be cell monitoring camera or unmanned plane of taking photo by plane etc. can to obtain region complete
The photographic device of whole image, it is noted that the image of described image acquisition device acquisition all should be the first area
Image without dead angle, all standing.Wherein, the situation to be detected does not carry out specially rubbish cleaning as, needs to carry out rubbish inspection
The case where survey.
For step S20, the benchmark image is rubbish cleaning specially to be carried out to the first area and by artificial inspection
It is obtained in the case that confirmation is without rubbish after looking by cell monitoring camera or unmanned plane of taking photo by plane.Optionally, the feature mentions
Take step that can carry out by Gabor filter.
For step S30, the similar preset threshold can be according to the benchmark image, the resolution of described image to be detected
Rate and the image-capable of the detection service device are adjusted.If point of the benchmark image, described image to be detected
Resolution is higher or the image-capable of the detection service device is higher, then can be by the similar preset threshold adaptability tune
It is high.
For step S40, the rubbish figure in described image to be detected is identified using convolutional neural networks object detection model
Picture, and the rubbish frames images are elected, determined in identification and when frame selects rubbish image the first area there are rubbish,
Determine that there is no rubbish for the first area when failing identification and frame selects rubbish image.
The present embodiment realizes the automation of the detection of the rubbish in cell or fixed area by step S10-S50, reduces
The human cost of artificial carry out rubbish detection, and the benchmark image is first based on before carrying out specific rubbish image recognition
Described image to be detected is tentatively judged with the presence or absence of rubbish with the similarity of the feature vector of described image to be detected, into
And the waste of unnecessary computing resource is avoided, improve the efficiency of rubbish detection；Further, above-mentioned steps also pass through volume
Product neural network object detection model carries out frame choosing and identification to rubbish image, improves the accuracy rate of rubbish identification.
It should be understood that as an implementation, carrying out step S10 and step S20 to the benchmark in first time
Before image is handled, first area described in detection service device control image acquisition device is further comprised the steps of: in no rubbish
Benchmark image in the case of rubbish.Wherein, the resolution ratio of the benchmark image, color depth, distortion situation etc. should with it is described to be checked
Altimetric image is consistent as far as possible.
As an implementation, before executing step S20 and carrying out characteristic vector pickup, the present embodiment is also to the base
Quasi- image and described image to be detected are pre-processed, to improve the accuracy of subsequent similarity-rough set, the pretreated step
It suddenly can be with are as follows: the detection service device carries out size normalization processing to the benchmark image and described image to be detected.Wherein,
In view of the resolution requirements of speed and rubbish the detection image processing of normalized, normalization size can be set to 256*
256。
Optionally, for step S30, it may be assumed that determine the similarity of the first eigenvector and the second feature vector
Less than default similar threshold value, by taking benchmark image A and image to be detected B as an example, benchmark image A and image to be detected B are being carried out greatly
Small normalization and feature extraction obtain the feature vector I of benchmark image AtrAnd the feature vector I of image to be detected Bte, utilize
Cosine similarity calculates ItrWith IteBetween similarity:If the similarity obtained is similar less than presetting
Threshold value then determines in described image to be detected there are rubbish, on the contrary then assert that there is no rubbish in described image to be detected.
It should be understood that the present embodiment judges institute using convolutional neural networks object detection model in execution step S40
It states in image to be detected with the presence or absence of before rubbish image, training is needed to obtain the convolutional neural networks object detection model.
As an implementation, referring to FIG. 2, Fig. 2 is a kind of convolutional neural networks object inspection that first embodiment of the invention provides
The flow diagram of model training step is surveyed, the convolutional neural networks object detection model training step can be such that
Step S31: the detection service device control described image acquisition device acquires several described first areas respectively and exists
In the case of no rubbish and there is the image in the case of rubbish as training sample.
Step S32: the detection service device obtains training set after carrying out data extending to the training sample.
Step S33: the detection service device is based on the training set and carries out convolutional neural networks object detection model training,
Obtain convolutional neural networks object detection model.
For step S32, optionally, the mode of the data extending can for mirror image, invert, cut out, color disturbance etc.,
The data extending scale of the present embodiment is 10 times, and data extending scale can be adjusted according to specific needs in other embodiments
It is whole.It works to avoid carrying out Image Acquisition in large quantities by the step S32, is reducing Image Acquisition workload, improving rubbish
Model training precision is improved while rubbish detection efficiency.
Optionally, for step S33, described " the detection service device is based on the training set and carries out convolutional neural networks
Object detection model training obtains convolutional neural networks object detection model " may include following sub-step: the detection service
Device carries out the pre- of garbage classification task to the archetype of convolutional neural networks object detection model on ImageNet data set
Training, the introductory die of the convolutional neural networks object detection model is obtained according to the model parameter value that the pre-training obtains
Type；The detection service device carries out arameter optimization to the initial model using the training set, obtains convolutional neural networks object
Body detection model.Obtain convolutional neural networks object detection model using ImageNet data set and training set, save into
Oneself carries out the time of image acquisition and mark when row initial model generates, and greatly improves convolutional neural networks object detection
The training effectiveness of model, while improving the adaptability and accuracy of convolutional neural networks object detection model.
There are rubbish images in image to be detected in view of confirming the first area, i.e., there are rubbish for the described first area
When rubbish, need to send corresponding notice or alarm to related personnel according to the concrete condition of rubbish.As an implementation, originally
Embodiment further includes rubbish alarm step after step S40.Referring to FIG. 3, Fig. 3 is one that first embodiment of the invention provides
The flow diagram of kind rubbish alarm step, step can be such that
Step S50: the detection service device determines rubbish according to position, type and the size of rubbish in the rubbish image
The grade of alarm.
Step S60: the detection service device issues the rubbish alarm of corresponding grade to default object.
Optionally, for step S50, specific steps be can be such that
Step S51: described image to be detected is divided into multiple subregions by the detection service device, and each subregion respectively corresponds
Different rubbish position weight value l identifies the subregion of rubbish corresponding position in described image to be detected, obtains described point
The corresponding rubbish position weight value l in area.
Step S52: the detection service device calculates the ratio s of the rubbish image Yu image to be detected size.
Step S53: different rubbish types is correspondingly arranged different rubbish class label c by the detection service device, is used
Depth network model identifies the rubbish type in the rubbish image.
Step S54: the detection service device obtains the rubbish of the rubbish by rubbish alert level formula θ=c (l+s)
Rubbish class parameter θ determines the grade of rubbish alarm according to the affiliated threshold interval of the rubbish class parameter θ.
For step S51, multiple subregions described in the present embodiment can be public area, angle when being divided for cell
It falls and garbage pool, the rubbish position weight value l being corresponding in turn to is respectively 0.6,0.3 and 0.1.It should be understood that above-mentioned subregion
Division can be adjusted according to the specific requirements of cell or community, corresponding rubbish position weight value l can also change therewith
Become.
Optionally, the depth network model can be GoogleNet depth network model, and before step S53,
It also needs to be trained the depth network model, training step can be with are as follows: the detection service device controls described image
Acquisition device acquires the rubbish image of a large amount of first areas, and marks out its rubbish type；The detection service device is to institute
State rubbish image carry out mirror image, overturn, cut out, color disturbance etc. completes data extending to obtaining training set；The detection clothes
Business device carries out the pre-training of classification task on ImageNet data set to the archetype of depth network model, according to described pre-
The model parameter value that training obtains obtains the initial model of the depth network model；The detection service device uses the training
Collection carries out arameter optimization to the initial model, obtains depth network model.
Optionally, c=4 when the rubbish type in step S53 is Harmful Waste, rubbish type are garbage or can be recycled
C=2 when rubbish, c=1 when rubbish type is Other Waste.
Further, described in operator can voluntarily adjust according to the concrete type of the first area and specific requirements
The specific value of rubbish class label and the rubbish position weight value.
Optionally, for step S54, the grade of rubbish alarm is determined according to rubbish class parameter θ, works as θ > θ1When be determined as
Level-one alarm, which sends out a warning and presets object to level-one, sends primary alarm (serious), works as θ2≤θ≤θ1When be determined as that secondary alarm is bright
Amber light simultaneously presets object transmission second-level alarm (medium) to second level, as θ < θ2When be determined as three-level alarm brilliant blue lamp and pre- to three-level
If object sends three-level alarm (slight), wherein θ1, θ2For the threshold values set according to concrete condition, setting rule is θ1>θ2, together
Shi Suoshu level-one presets the default object of object, second level, the default object of three-level can be set as property contact person according to specific requirements,
Cleaning group contact person etc..
The present embodiment completes the judgement to rubbish type by above embodiment, reduces the detection of later period rubbish and rubbish
The workload of classification, and determine according to the type of the rubbish, position and size the extent of injury of the rubbish, and to correlation
Personnel send rubbish alarm corresponding with the extent of injury, thus to detecting that there are can when rubbish for the first area
Timely information is carried out to corresponding personnel to feed back, to improve garbage disposal response speed, improves the environment of first area
Clean and tidy degree.
Second embodiment
In order to cooperate the rubbish detection method in first embodiment of the invention, second embodiment of the invention additionally provides one kind
Rubbish detection device 100.
Referring to FIG. 4, Fig. 4 is a kind of module diagram for rubbish detection device that second embodiment of the invention provides.
Rubbish detection device 100 includes image collection module 110, characteristic extracting module 120, similarity judgment module 130
With rubbish judgment module 140.
Image collection module 110 is for obtaining the image to be detected of first area in situation to be detected.Feature extraction mould
Block 120 is used for the benchmark image to the first area in no rubbish and carries out feature extraction acquisition first eigenvector,
Feature extraction is carried out to described image to be detected and obtains second feature vector.Similarity judgment module 130 is for determining described the
One feature vector and the similarity of the second feature vector are less than default similar threshold value.Rubbish judgment module 140 is for using
Convolutional neural networks object detection model judges in described image to be detected with the presence or absence of rubbish image, if so, determining described the
There are rubbish in one region.
As an implementation, rubbish detection device 100 further includes that training sample obtains module, training set obtains module
With convolutional neural networks object detection model training module.The training sample obtain module for obtain respectively several described the
One region is in no rubbish and has the image in the case of rubbish as training sample.The training set obtain module for pair
The training sample obtains training set after carrying out data extending.The convolutional neural networks object detection model training module is used for
Convolutional neural networks object detection model training is carried out based on the training set, obtains convolutional neural networks object detection model.
Further, in order to carry out classification and the automatic alarm of rubbish, rubbish detection device 100 can also include that position is weighed
Weight values computing module, image size computing module, rubbish category identification module and rubbish alert level determining module.The position
Weight value calculation module is used to described image to be detected being divided into multiple subregions, and each subregion respectively corresponds different rubbish positions
Weighted value l is set, identifies the subregion of rubbish corresponding position in described image to be detected, obtains the corresponding rubbish of the subregion
Position weight value l.Described image size computing module is used to calculate the ratio of the rubbish image and image to be detected size
Value s.The rubbish category identification module, for different rubbish types to be correspondingly arranged to different rubbish class label c, using depth
Degree network model identifies the rubbish type in the rubbish image.The rubbish alert level determining module is used for alert by rubbish
Hierarchy equation θ=c (l+s) is reported to obtain the rubbish class parameter θ of the rubbish, according to the affiliated threshold of the rubbish class parameter θ
Value section determines the grade of rubbish alarm.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description
Specific work process, no longer can excessively be repeated herein with reference to the corresponding process in preceding method.
3rd embodiment
Referring to figure 5., Fig. 5 is a kind of electronics that can be applied in the embodiment of the present application that third embodiment of the invention provides
The structural block diagram of equipment.Electronic equipment 200 may include rubbish detection device 100, memory 201, storage control 202, place
Manage device 203, Peripheral Interface 204, input-output unit 205, audio unit 206, display unit 207.
The memory 201, storage control 202, processor 203, Peripheral Interface 204, input-output unit 205, sound
Frequency unit 206, each element of display unit 207 are directly or indirectly electrically connected between each other, to realize the transmission or friendship of data
Mutually.It is electrically connected for example, these elements can be realized between each other by one or more communication bus or signal wire.The rubbish
Detection device 100 include at least one can be stored in the form of software or firmware (firmware) in the memory 201 or
The software function module being solidificated in the operating system (operating system, OS) of rubbish detection device 100.The processing
Device 203 is for executing the executable module stored in memory 201, such as the software function mould that rubbish detection device 100 includes
Block or computer program.
Wherein, memory 201 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only
Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM),
Electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..
Wherein, memory 201 is for storing program, and the processor 203 executes described program after receiving and executing instruction, aforementioned
Method performed by the server that the stream process that any embodiment of the embodiment of the present invention discloses defines can be applied to processor 203
In, or realized by processor 203.
Processor 203 can be a kind of IC chip, the processing capacity with signal.Above-mentioned processor 203 can
To be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network processing unit
(Network Processor, abbreviation NP) etc.；Can also be digital signal processor (DSP), specific integrated circuit (ASIC),
Ready-made programmable gate array (FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hard
Part component.It may be implemented or execute disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor
It can be microprocessor or the processor 203 be also possible to any conventional processor etc..
Various input/output devices are couple processor 203 and memory 201 by the Peripheral Interface 204.Some
In embodiment, Peripheral Interface 204, processor 203 and storage control 202 can be realized in one single chip.Other one
In a little examples, they can be realized by independent chip respectively.
Input-output unit 205 realizes user and the server (or local terminal) for being supplied to user input data
Interaction.The input-output unit 205 may be, but not limited to, the equipment such as mouse and keyboard.
Audio unit 206 provides a user audio interface, may include one or more microphones, one or more raises
Sound device and voicefrequency circuit.
Display unit 207 provides an interactive interface (such as user's operation circle between the electronic equipment 200 and user
Face) or for display image data give user reference.In the present embodiment, the display unit 207 can be liquid crystal display
Or touch control display.It can be the capacitance type touch control screen or resistance of support single-point and multi-point touch operation if touch control display
Formula touch screen etc..Single-point and multi-point touch operation is supported to refer to that touch control display can sense on the touch control display one
Or at multiple positions simultaneously generate touch control operation, and the touch control operation that this is sensed transfer to processor 203 carry out calculate and
Processing.
It is appreciated that structure shown in fig. 5 is only to illustrate, the electronic equipment 200 may also include more than shown in Fig. 5
Perhaps less component or with the configuration different from shown in Fig. 5.Each component shown in Fig. 5 can use hardware, software
Or combinations thereof realize.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description
Specific work process, no longer can excessively be repeated herein with reference to the corresponding process in preceding method.
In conclusion the rubbish detection method uses first the embodiment of the invention provides a kind of rubbish detection method
The image to be detected for needing to carry out rubbish detection of image of the region in no rubbish as benchmark image and subsequent acquisition
Image procossing is carried out, rubbish detection is carried out by computer based on image procossing and neural network model automatically, improves rubbish
The efficiency and accuracy of detection, reduce cost of labor.Wherein, the rubbish detection method is carried out using neural network model
Before the judgement of rubbish image, also the feature vector similarity of the benchmark image and described image to be detected is compared, is determined
The similarity just executes the rubbish image judgement when being greater than preset threshold, avoid directlying adopt neural network model progress rubbish
Rubbish image judges and wastes a large amount of computing resource, further improves the efficiency and resource utilization of the rubbish detection.Together
When, judged with the presence or absence of rubbish image using convolutional neural networks object detection model in described image to be detected, energy
Enough the rubbish image in described image to be detected is known by the frame recurrence in convolutional neural networks object detection model
It is not selected with frame, improves the accuracy of rubbish detection.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part of the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that function marked in the box can also be to be different from some implementations as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each functional module in each embodiment of the present invention can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-OnlyMemory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should also be noted that similar label and letter exist
Similar terms are indicated in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and explained.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
Claims (10)
1. a kind of rubbish detection method, which is characterized in that the rubbish detection method includes:
Acquire the image to be detected of first area in situation to be detected；
Feature extraction is carried out to benchmark image of the first area in no rubbish and obtains first eigenvector, to described
Image to be detected carries out feature extraction and obtains second feature vector；
Determine that the similarity of the first eigenvector and the second feature vector is less than default similar threshold value；
Judged using convolutional neural networks object detection model with the presence or absence of rubbish image in described image to be detected, if so, really
There are rubbish for the fixed first area.
2. rubbish detection method according to claim 1, which is characterized in that it is described to the first area in no rubbish
In the case of benchmark image carry out feature extraction obtain first eigenvector before, the rubbish detection method further include:
Acquire benchmark image of the first area in no rubbish.
3. rubbish detection method according to claim 1, which is characterized in that in the acquisition first area in feelings to be detected
After image to be detected under condition and the benchmark image to the first area in no rubbish carries out feature and mentions
Acquisition first eigenvector is taken, before carrying out feature extraction acquisition second feature vector to described image to be detected, the rubbish
Detection method further include:
Size normalization processing is carried out to the benchmark image and described image to be detected.
4. rubbish detection method according to claim 1, which is characterized in that examined described using convolutional neural networks object
Before survey model judges to whether there is rubbish image in described image to be detected, the rubbish detection method further include:
Several described first areas are acquired respectively in no rubbish and have the image in the case of rubbish as training sample；
Training set is obtained after carrying out data extending to the training sample；
Convolutional neural networks object detection model training is carried out based on the training set, obtains convolutional neural networks object detection mould
Type.
5. rubbish detection method according to claim 4, which is characterized in that described to carry out data expansion to the training sample
Training set is obtained after filling, comprising:
Mirror image is carried out to the training sample, inverts, cut out, obtain training set after the data extending of color disturbance.
6. rubbish detection method according to claim 4, which is characterized in that described to carry out convolution mind based on the training set
Through network object detection model training, comprising:
Garbage classification task is carried out to the archetype of convolutional neural networks object detection model on ImageNet data set
Pre-training obtains the initial model of convolutional neural networks object detection model according to the model parameter value that the pre-training obtains；
Arameter optimization is carried out to the initial model using the training set, obtains convolutional neural networks object detection model.
7. rubbish detection method according to claim 1, which is characterized in that in the determination first area, there are rubbish
After rubbish, the rubbish detection method further include:
The grade of rubbish alarm is determined according to position, type and the size of rubbish in the rubbish image；
The rubbish alarm of corresponding grade is issued to default object.
8. rubbish detection method according to claim 7, which is characterized in that described according to rubbish in the rubbish image
Position, type and size determine rubbish alarm grade before, the rubbish detection method further include:
Described image to be detected is divided into multiple subregions, each subregion respectively corresponds different rubbish position weight value l, identification
The subregion of rubbish corresponding position in described image to be detected obtains the corresponding rubbish position weight value l of the subregion；
Calculate the ratio s of the rubbish image Yu image to be detected size；
Different rubbish types is correspondingly arranged to different rubbish class label c, the rubbish figure is identified using depth network model
Rubbish type as in；
The rubbish class parameter θ that the rubbish is obtained by rubbish alert level formula θ=c (l+s), according to described rubbish etc.
The grade affiliated threshold interval of parameter θ determines the grade of rubbish alarm.
9. a kind of rubbish detection device, which is characterized in that the rubbish detection device includes:
Image collection module obtains the image to be detected of first area in situation to be detected；
Characteristic extracting module obtains for carrying out feature extraction to benchmark image of the first area in no rubbish
One feature vector carries out feature extraction to described image to be detected and obtains second feature vector；
Similarity judgment module, for determining that it is default that the similarity of the first eigenvector and the second feature vector is less than
Similar threshold value；
Rubbish judgment module, for judging to whether there is in described image to be detected using convolutional neural networks object detection model
Rubbish image, if so, determining that there are rubbish for the first area.
10. a kind of storage medium, which is characterized in that be stored with computer program instructions, the computer in the storage medium
When program instruction is read and run by a processor, perform claim requires the step in any one of 1-8 the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810951193.7A CN108932510A (en) | 2018-08-20 | 2018-08-20 | A kind of rubbish detection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810951193.7A CN108932510A (en) | 2018-08-20 | 2018-08-20 | A kind of rubbish detection method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108932510A true CN108932510A (en) | 2018-12-04 |
Family
ID=64446178
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810951193.7A Pending CN108932510A (en) | 2018-08-20 | 2018-08-20 | A kind of rubbish detection method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108932510A (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109948506A (en) * | 2019-03-14 | 2019-06-28 | 南通大学 | A multi-angle garbage classification cloud platform based on deep learning |
| CN110108765A (en) * | 2019-05-28 | 2019-08-09 | 浙江科技学院 | Bagged garbage dry and wet recognition methods |
| CN110427838A (en) * | 2019-07-13 | 2019-11-08 | 恒大智慧科技有限公司 | A kind of rubbish method for cleaning, computer equipment and readable storage medium storing program for executing |
| CN110490142A (en) * | 2019-08-21 | 2019-11-22 | 北京百度网讯科技有限公司 | For generating the method and device of alarm signal |
| CN110659622A (en) * | 2019-09-27 | 2020-01-07 | 北京文安智能技术股份有限公司 | Detection method, device and system for garbage dumping |
| CN110688945A (en) * | 2019-09-26 | 2020-01-14 | 成都睿云物联科技有限公司 | Cleanliness detection method and device, computer equipment and storage medium |
| CN110956104A (en) * | 2019-11-20 | 2020-04-03 | 河南华衍智能科技有限公司 | Method, device and system for detecting overflow of garbage can |
| CN111017429A (en) * | 2019-11-20 | 2020-04-17 | 重庆特斯联智慧科技股份有限公司 | Community garbage classification method and system based on multi-factor fusion |
| CN111151368A (en) * | 2020-01-09 | 2020-05-15 | 珠海格力电器股份有限公司 | Garbage treatment method, system, storage medium and garbage treatment equipment |
| CN111767822A (en) * | 2020-06-23 | 2020-10-13 | 浙江大华技术股份有限公司 | Garbage detection method and related equipment and device |
| CN112183460A (en) * | 2020-10-20 | 2021-01-05 | 武汉光谷联合集团有限公司 | Method and device for intelligently identifying environmental sanitation |
| CN112298844A (en) * | 2019-07-29 | 2021-02-02 | 杭州海康威视数字技术股份有限公司 | Garbage classification monitoring method and device |
| CN112315383A (en) * | 2020-10-29 | 2021-02-05 | 上海高仙自动化科技发展有限公司 | Inspection cleaning method and device for robot, robot and storage medium |
| CN112668423A (en) * | 2020-12-18 | 2021-04-16 | 平安科技（深圳）有限公司 | Corridor sundry detection method and device, terminal equipment and storage medium |
| CN112686162A (en) * | 2020-12-31 | 2021-04-20 | 北京每日优鲜电子商务有限公司 | Method, device, equipment and storage medium for detecting clean state of warehouse environment |
| JP2021159881A (en) * | 2020-04-01 | 2021-10-11 | Ｊｘ金属株式会社 | Composition analysis method for electronic/electrical equipment component scrap, disposal method for electronic/electrical equipment component scrap, analysis device for electronic/electrical equipment component scrap, and processing device for electronic/electrical equipment component scrap |
| CN113642365A (en) * | 2020-05-11 | 2021-11-12 | 上海厚熠网络科技有限公司 | Community garbage monitoring system and method thereof |
| CN114821264A (en) * | 2022-03-28 | 2022-07-29 | 慧之安信息技术股份有限公司 | Algorithm efficiency improving method based on neural network |
| WO2022174541A1 (en) * | 2021-02-20 | 2022-08-25 | 北京市商汤科技开发有限公司 | Garbage detection method and apparatus, device, storage medium, and program product |
| JP2023510063A (en) * | 2020-12-18 | 2023-03-13 | 平安科技（深▲せん▼）有限公司 | Method, apparatus, terminal device and storage medium for detecting debris in corridor |
| CN117952967A (en) * | 2024-03-26 | 2024-04-30 | 广东先知大数据股份有限公司 | Dustbin region abnormality detection method, electronic equipment and storage medium |
| CN118134467A (en) * | 2024-03-25 | 2024-06-04 | 航天规划设计集团有限公司 | Construction waste treatment recycling method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787506A (en) * | 2016-01-26 | 2016-07-20 | 耿春茂 | Garbage classification and evaluation method based on image recognition and two-dimensional code recognition technology |
| CN106203498A (en) * | 2016-07-07 | 2016-12-07 | 中国科学院深圳先进技术研究院 | A kind of City scenarios rubbish detection method and system |
| CN106358021A (en) * | 2016-11-01 | 2017-01-25 | 成都宏软科技实业有限公司 | Smart community monitoring system |
| CN106845408A (en) * | 2017-01-21 | 2017-06-13 | 浙江联运知慧科技有限公司 | A kind of street refuse recognition methods under complex environment |
| CN107092914A (en) * | 2017-03-23 | 2017-08-25 | 广东数相智能科技有限公司 | Refuse classification method, device and system based on image recognition |
| CN107145842A (en) * | 2017-04-19 | 2017-09-08 | 西安电子科技大学 | Face Recognition Method Combining LBP Feature Map and Convolutional Neural Network |
| CN107866386A (en) * | 2017-09-30 | 2018-04-03 | 四川绿能环保科技股份公司 | Perishable waste identification system and method |
-
2018
- 2018-08-20 CN CN201810951193.7A patent/CN108932510A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787506A (en) * | 2016-01-26 | 2016-07-20 | 耿春茂 | Garbage classification and evaluation method based on image recognition and two-dimensional code recognition technology |
| CN106203498A (en) * | 2016-07-07 | 2016-12-07 | 中国科学院深圳先进技术研究院 | A kind of City scenarios rubbish detection method and system |
| CN106358021A (en) * | 2016-11-01 | 2017-01-25 | 成都宏软科技实业有限公司 | Smart community monitoring system |
| CN106845408A (en) * | 2017-01-21 | 2017-06-13 | 浙江联运知慧科技有限公司 | A kind of street refuse recognition methods under complex environment |
| CN107092914A (en) * | 2017-03-23 | 2017-08-25 | 广东数相智能科技有限公司 | Refuse classification method, device and system based on image recognition |
| CN107145842A (en) * | 2017-04-19 | 2017-09-08 | 西安电子科技大学 | Face Recognition Method Combining LBP Feature Map and Convolutional Neural Network |
| CN107866386A (en) * | 2017-09-30 | 2018-04-03 | 四川绿能环保科技股份公司 | Perishable waste identification system and method |
Non-Patent Citations (2)
| Title |
|---|
| MOHAMMAD SAEED RAD: "A Computer Vision System to Localize and Classify Wastes on the Streets", 《INTERNATIONAL CONFERENCE ON COMPUTER VISION SYSTEM》 * |
| 魏书法: "基于图像的城市场景垃圾自动检测", 《集成技术》 * |
Cited By (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109948506A (en) * | 2019-03-14 | 2019-06-28 | 南通大学 | A multi-angle garbage classification cloud platform based on deep learning |
| CN110108765A (en) * | 2019-05-28 | 2019-08-09 | 浙江科技学院 | Bagged garbage dry and wet recognition methods |
| CN110108765B (en) * | 2019-05-28 | 2021-08-03 | 浙江科技学院 | Bagged garbage dry and wet identification method |
| CN110427838A (en) * | 2019-07-13 | 2019-11-08 | 恒大智慧科技有限公司 | A kind of rubbish method for cleaning, computer equipment and readable storage medium storing program for executing |
| CN112298844B (en) * | 2019-07-29 | 2023-09-22 | 杭州海康威视数字技术股份有限公司 | Garbage classification supervision method and device |
| CN112298844A (en) * | 2019-07-29 | 2021-02-02 | 杭州海康威视数字技术股份有限公司 | Garbage classification monitoring method and device |
| CN110490142A (en) * | 2019-08-21 | 2019-11-22 | 北京百度网讯科技有限公司 | For generating the method and device of alarm signal |
| CN110490142B (en) * | 2019-08-21 | 2022-08-23 | 北京百度网讯科技有限公司 | Method and device for generating alarm signal |
| CN110688945A (en) * | 2019-09-26 | 2020-01-14 | 成都睿云物联科技有限公司 | Cleanliness detection method and device, computer equipment and storage medium |
| CN110659622A (en) * | 2019-09-27 | 2020-01-07 | 北京文安智能技术股份有限公司 | Detection method, device and system for garbage dumping |
| CN111017429A (en) * | 2019-11-20 | 2020-04-17 | 重庆特斯联智慧科技股份有限公司 | Community garbage classification method and system based on multi-factor fusion |
| CN110956104A (en) * | 2019-11-20 | 2020-04-03 | 河南华衍智能科技有限公司 | Method, device and system for detecting overflow of garbage can |
| CN111151368A (en) * | 2020-01-09 | 2020-05-15 | 珠海格力电器股份有限公司 | Garbage treatment method, system, storage medium and garbage treatment equipment |
| JP2021159881A (en) * | 2020-04-01 | 2021-10-11 | Ｊｘ金属株式会社 | Composition analysis method for electronic/electrical equipment component scrap, disposal method for electronic/electrical equipment component scrap, analysis device for electronic/electrical equipment component scrap, and processing device for electronic/electrical equipment component scrap |
| JP7301783B2 (en) | 2020-04-01 | 2023-07-03 | Ｊｘ金属株式会社 | Composition analysis method for electronic/electrical equipment parts scrap, electronic/electrical equipment parts scrap processing method, electronic/electrical equipment parts scrap composition analysis device, and electronic/electrical equipment parts scrap processing equipment |
| CN113642365A (en) * | 2020-05-11 | 2021-11-12 | 上海厚熠网络科技有限公司 | Community garbage monitoring system and method thereof |
| CN111767822B (en) * | 2020-06-23 | 2023-04-25 | 浙江大华技术股份有限公司 | Garbage detection method, related equipment and device |
| CN111767822A (en) * | 2020-06-23 | 2020-10-13 | 浙江大华技术股份有限公司 | Garbage detection method and related equipment and device |
| CN112183460A (en) * | 2020-10-20 | 2021-01-05 | 武汉光谷联合集团有限公司 | Method and device for intelligently identifying environmental sanitation |
| CN112315383A (en) * | 2020-10-29 | 2021-02-05 | 上海高仙自动化科技发展有限公司 | Inspection cleaning method and device for robot, robot and storage medium |
| CN112668423B (en) * | 2020-12-18 | 2024-05-28 | 平安科技（深圳）有限公司 | Corridor sundry detection method and device, terminal equipment and storage medium |
| US12067792B2 (en) | 2020-12-18 | 2024-08-20 | Ping An Technology (Shenzhen) Co., Ltd. | Method for detecting groceries in corridor, terminal device and storage medium |
| JP2023510063A (en) * | 2020-12-18 | 2023-03-13 | 平安科技（深▲せん▼）有限公司 | Method, apparatus, terminal device and storage medium for detecting debris in corridor |
| JP7258188B2 (en) | 2020-12-18 | 2023-04-14 | 平安科技（深▲せん▼）有限公司 | Method, apparatus, terminal device and storage medium for detecting debris in corridor |
| WO2022126908A1 (en) * | 2020-12-18 | 2022-06-23 | 平安科技（深圳）有限公司 | Corridor sundries detection method and apparatus, and terminal device and storage medium |
| CN112668423A (en) * | 2020-12-18 | 2021-04-16 | 平安科技（深圳）有限公司 | Corridor sundry detection method and device, terminal equipment and storage medium |
| CN112686162A (en) * | 2020-12-31 | 2021-04-20 | 北京每日优鲜电子商务有限公司 | Method, device, equipment and storage medium for detecting clean state of warehouse environment |
| CN112686162B (en) * | 2020-12-31 | 2023-12-15 | 鄂尔多斯市空港大数据运营有限公司 | Method, device, equipment and storage medium for detecting clean state of warehouse environment |
| WO2022174541A1 (en) * | 2021-02-20 | 2022-08-25 | 北京市商汤科技开发有限公司 | Garbage detection method and apparatus, device, storage medium, and program product |
| CN114821264A (en) * | 2022-03-28 | 2022-07-29 | 慧之安信息技术股份有限公司 | Algorithm efficiency improving method based on neural network |
| CN114821264B (en) * | 2022-03-28 | 2023-02-17 | 慧之安信息技术股份有限公司 | Algorithm efficiency improving method based on neural network |
| CN118134467A (en) * | 2024-03-25 | 2024-06-04 | 航天规划设计集团有限公司 | Construction waste treatment recycling method and system |
| CN118134467B (en) * | 2024-03-25 | 2024-09-06 | 航天规划设计集团有限公司 | Construction waste treatment recycling method and system |
| CN117952967A (en) * | 2024-03-26 | 2024-04-30 | 广东先知大数据股份有限公司 | Dustbin region abnormality detection method, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108932510A (en) | A kind of rubbish detection method and device | |
| CN109344894B (en) | Garbage classification and identification method and device based on multi-sensor fusion and deep learning | |
| CN109201514B (en) | Waste sorting recycle method, garbage classification device and classified-refuse recovery system | |
| CN111126252B (en) | Swing behavior detection method and related device | |
| Taubenböck et al. | Object-based feature extraction using high spatial resolution satellite data of urban areas | |
| CN113822247B (en) | Method and system for identifying illegal building based on aerial image | |
| CN109871730A (en) | A kind of target identification method, device and monitoring device | |
| CN105025440B (en) | Indoor and outdoor scene detection method and equipment | |
| CN110245651B (en) | Quality intelligent detection method and relevant apparatus | |
| CN111210399B (en) | Imaging quality evaluation method, device and equipment | |
| CN106651392A (en) | Intelligent business location selection method, apparatus and system | |
| WO2022174541A1 (en) | Garbage detection method and apparatus, device, storage medium, and program product | |
| CN107194396A (en) | Method for early warning is recognized based on the specific architecture against regulations in land resources video monitoring system | |
| CN106203498A (en) | A kind of City scenarios rubbish detection method and system | |
| CN108460362A (en) | A kind of system and method for detection human body | |
| CN113112480B (en) | Video scene change detection method, storage medium and electronic device | |
| CN109064446A (en) | Display screen quality determining method, device, electronic equipment and storage medium | |
| CN105323024B (en) | A kind of network signal intensity detection and fusion method | |
| CN113468976B (en) | Garbage detection method, garbage detection system, and computer-readable storage medium | |
| CN108510007A (en) | A kind of webpage tamper detection method, device, electronic equipment and storage medium | |
| CN110084232A (en) | The recognition methods of chinese character, device and terminal device in license plate | |
| CN102867214B (en) | Counting management method for people within area range | |
| CN109492573A (en) | A kind of pointer read method and device | |
| da Fonseca Feitosa et al. | IMMerSe: An integrated methodology for mapping and classifying precarious settlements | |
| CN108764365A (en) | A kind of device signboard detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| AD01 | Patent right deemed abandoned | ||
| AD01 | Patent right deemed abandoned |
Effective date of abandoning: 20210409 |