CN114494795B - Parking detection method, device, equipment and storage medium based on chassis detection - Google Patents
Parking detection method, device, equipment and storage medium based on chassis detection Download PDFInfo
- Publication number
- CN114494795B CN114494795B CN202111577660.2A CN202111577660A CN114494795B CN 114494795 B CN114494795 B CN 114494795B CN 202111577660 A CN202111577660 A CN 202111577660A CN 114494795 B CN114494795 B CN 114494795B
- Authority
- CN
- China
- Prior art keywords
- chassis
- body frame
- parking
- detection
- vehicle body
- 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.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 167
- 238000012549 training Methods 0.000 claims abstract description 49
- 238000003062 neural network model Methods 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000013135 deep learning Methods 0.000 claims abstract description 11
- 230000015654 memory Effects 0.000 claims description 18
- 238000012544 monitoring process Methods 0.000 claims description 18
- 238000012360 testing method Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 description 15
- 238000012545 processing Methods 0.000 description 8
- 230000002093 peripheral effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a parking detection method, a device, equipment and a storage medium based on chassis detection, wherein the method comprises the steps of acquiring a training image data set marked with the chassis position on a vehicle body frame; inputting the training image data set into a neural network model for training to obtain a chassis detection model; and detecting the chassis position in the target car body frame image by using the chassis detection model, and determining the parking information of the vehicle based on the vertex position of the chassis and the standard position of the parking space. According to the invention, the chassis position of the original image data set of the vehicle body frame is marked, and the chassis detection model is further trained to obtain the vertex position information of the chassis when the vehicle body frame image is detected, the parking information of the vehicle is determined according to the vertex position and the standard position of the parking space, and the parking information of the vehicle is determined by deep learning and recognition of the position of the chassis relative to the vehicle body frame, so that inaccurate vehicle position detection caused by a camera perspective relationship is avoided, and the accuracy of a parking detection event is improved.
Description
Technical Field
The present invention relates to the field of image recognition technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting parking based on chassis detection.
Background
At present, automobiles are almost one of the necessary tools for each family to go out, the number of automobiles is continuously increased, and in order to optimize the parking experience of users, parking lots need to detect the parking event and the parking position of vehicles entering the parking lots so as to improve the use efficiency of the parking lots.
At present, equipment such as camera shooting monitoring is generally adopted for event detection in a parking lot, however, due to perspective relation of a camera, a vehicle detection frame often cannot truly reflect the position and state of a vehicle in a real space, so that judgment of a parking relation is easy to make mistakes. Therefore, how to improve the detection accuracy of the parking detection event is a technical problem to be solved.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a parking detection method, device, equipment and storage medium based on chassis detection, and aims to solve the technical problem that the detection accuracy of the existing parking detection event is low.
In order to achieve the above object, the present invention provides a parking detection method based on chassis detection, the method comprising the steps of:
Acquiring an original image data set of a vehicle body frame, and marking the chassis position in the original image data set to obtain a training image data set of the vehicle body frame;
Inputting the training image data set into a neural network model for training to obtain a chassis detection model;
when a target car body frame image is received, detecting the position of a chassis in the target car body frame image by using the chassis detection model, and obtaining vertex position information of the chassis in the target car body frame image;
And acquiring standard position information of the target parking space, and determining parking information of the vehicle based on the vertex position information and the standard position information.
According to the invention, the parking information of the vehicle is determined by deep learning and recognition of the position of the chassis relative to the vehicle body frame, so that the problem of inaccurate vehicle position detection caused by a camera perspective relationship is avoided, and the accuracy of a parking detection event is improved.
Optionally, the step of obtaining the original image dataset of the vehicle body frame and marking the chassis position in the original image dataset to obtain the training image dataset of the vehicle body frame specifically includes:
acquiring an original image data set of a vehicle body frame, extracting the characteristics of the vehicle body frame in the original image data set, and marking the position of a chassis corresponding to the characteristics of the vehicle body frame;
And building a training image data set of the vehicle body frame by utilizing the characteristics of the vehicle body frame and the positions of the corresponding chassis.
In the invention, when marking is carried out, the features of the vehicle body frame in the original image data set can be firstly extracted, the position of the chassis is marked, the vehicle body frame is used for building a training image data set of the vehicle body frame, and then the chassis detection model is trained, the chassis position of a single image is determined through the feature detection of the vehicle body frame and the marking of the chassis, and the detection efficiency is improved
Optionally, the step of inputting the training image dataset into a neural network model for training to obtain a chassis detection model specifically includes:
inputting the characteristics of the vehicle body frame and the positions of the corresponding chassis into a neural network model for training to obtain a chassis detection model; the neural network model is a deep learning regression model.
In the invention, a deep learning regression model is adopted to train the positions of a vehicle body frame and a chassis corresponding to the vehicle body frame so as to obtain a chassis detection model.
Optionally, the parking detection method based on chassis detection further includes:
Acquiring a monitoring video; wherein, the monitoring video records frame images of the target parking space;
And judging whether the frame image has the characteristics of the vehicle body frame or not, if so, extracting the frame image with the characteristics of the vehicle body frame to obtain a test image data set of the vehicle body frame.
In the invention, before the chassis detection model detects, a monitoring video is also required to be acquired, and a frame image with the characteristics of the vehicle body frame in the monitoring video is extracted, so that a test image dataset of the vehicle body frame is determined, and the frame image without the characteristics of the vehicle body frame is removed, thereby improving the chassis detection efficiency.
Optionally, when the target car body frame image is received, detecting a chassis position in the target car body frame image by using the chassis detection model, and obtaining vertex position information of a chassis in the target car body frame image, including:
When a test image data set is received, judging whether the stop moving time of a target vehicle in the test image data set exceeds a preset value, and if so, determining a target vehicle body frame image;
And detecting the chassis position in the target car body frame image by using the chassis detection model, and obtaining the vertex position information of the chassis in the target car body frame image.
According to the invention, whether the frame image is the target car body frame image is judged by the time of stopping the movement of the target car, and the frame image of the target car which is not stopped in a moving way is removed, so that the detection efficiency of chassis detection of the target car is improved.
Optionally, the step of obtaining the standard position information of the target parking space and determining the parking information of the vehicle based on the vertex position information and the standard position information specifically includes:
obtaining standard position information of a target parking space; the standard position information comprises the vertex position and/or the boundary position of the target parking space;
And monitoring whether the vertex position information meets the vertex position parking condition or the boundary position parking condition, and if so, determining the parking information of the vehicle.
According to the invention, the judgment of the parking information is realized through the standard position information and the vertexes or boundaries of the vertex position information, and the parking detection error caused by the abnormal parking of the vehicle is avoided.
Optionally, the vertex position parking condition is that the sum of the distance between the vertex position of the chassis in the target vehicle body frame image and the vertex position corresponding to the target parking space is smaller than a first preset value; and the boundary position parking condition is that the sum of the distance between the vertex position of the chassis in the target car body frame image and the boundary position corresponding to the target parking space is smaller than a second preset value.
According to the invention, the parking event of the vehicle is judged through the standard position information and the vertex-to-vertex and vertex-to-boundary distances in the vertex position information, so that the judgment accuracy and the fault tolerance to the vehicle parking deviation are improved.
In addition, in order to achieve the above object, the present invention also provides a parking detection device based on chassis detection, including:
The marking module is used for acquiring an original image data set of the vehicle body frame, marking the chassis position in the original image data set and acquiring a training image data set of the vehicle body frame;
The training module is used for inputting the training image data set into a neural network model for training to obtain a chassis detection model;
the detection module is used for detecting the chassis position in the target car body frame image by using the chassis detection model when the target car body frame image is received, and obtaining the vertex position information of the chassis in the target car body frame image;
and the determining module is used for acquiring the standard position information of the target parking space and determining the parking information of the vehicle based on the vertex position information and the standard position information.
In addition, in order to achieve the above object, the present invention also provides a chassis detection-based parking detection apparatus including: the system comprises a memory, a processor and a chassis detection based parking detection program stored on the memory and capable of running on the processor, wherein the chassis detection based parking detection program realizes the steps of the chassis detection based parking detection method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a storage medium having stored thereon a chassis detection-based parking detection program which, when executed by a processor, implements the steps of the chassis detection-based parking detection method as described above.
The embodiment of the invention provides a parking detection method, device, equipment and storage medium based on chassis detection, wherein the method comprises the steps of obtaining an original image data set of a vehicle body frame, marking the chassis position in the original image data set, and obtaining a training image data set of the vehicle body frame; inputting the training image data set into a neural network model for training to obtain a chassis detection model; when a target car body frame image is received, detecting the position of a chassis in the target car body frame image by using the chassis detection model, and obtaining vertex position information of the chassis in the target car body frame image; and acquiring standard position information of the target parking space, and determining parking information of the vehicle based on the vertex position information and the standard position information. According to the invention, the chassis position of the original image data set of the vehicle body frame is marked, and the chassis detection model is further trained and obtained, so that the vertex position information of the chassis is obtained when the vehicle body frame image is detected, the parking information of the vehicle is determined according to the vertex position information and the standard position information of the parking space, and the parking information of the vehicle is determined by deep learning and recognition of the position of the chassis relative to the vehicle body frame, so that the problem of inaccurate vehicle position detection caused by a camera perspective relationship is avoided, and the accuracy of a parking detection event is improved.
Drawings
Fig. 1 is a schematic structural diagram of a parking detection device based on chassis detection in an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of a chassis detection based parking detection method of the present invention;
fig. 3 is a block diagram of a parking detection device based on chassis detection in an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
At present, automobiles are almost one of the necessary tools for each family to go out, the number of automobiles is continuously increased, and in order to optimize the parking experience of users, parking lots need to detect the parking event and the parking position of vehicles entering the parking lots so as to improve the use efficiency of the parking lots.
At present, equipment such as camera shooting monitoring is generally adopted for event detection in a parking lot, however, due to perspective relation of a camera, a vehicle detection frame often cannot truly reflect the position and state of a vehicle in a real space, so that judgment of a parking relation is easy to make mistakes. Therefore, how to improve the detection accuracy of the parking detection event is a technical problem to be solved.
To solve this problem, various embodiments of the chassis detection-based parking detection method of the present invention are proposed. According to the parking detection method based on chassis detection, the chassis position of the original image dataset of the vehicle body frame is marked, the chassis detection model is further trained and obtained, so that the vertex position information of the chassis is obtained when the vehicle body frame image is detected, the parking information of a vehicle is determined according to the vertex position information and the standard position information of a parking space, and the parking information of the vehicle is determined by deep learning and recognition of the position of the chassis relative to the vehicle body frame, so that the problem of inaccurate vehicle position detection caused by camera perspective relation is avoided, and the accuracy of a parking detection event is improved.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a parking detection device based on chassis detection according to an embodiment of the present invention.
The device may be a Mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), or other User Equipment (UE), a handheld device, an in-vehicle device, a wearable device, a computing device, or other processing device connected to a wireless modem, a Mobile Station (MS), or the like. The device may be referred to as a user terminal, portable terminal, desktop terminal, etc.
Generally, an apparatus comprises: at least one processor 301, a memory 302 and a chassis detection based parking detection program stored on said memory and executable on said processor, said chassis detection based parking detection program being configured to implement the steps of the chassis detection based parking detection method as described above.
Processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 301 may be implemented in at least one hardware form of DSP (DIGITAL SIGNAL Processing), FPGA (Field-Programmable gate array), PLA (Programmable Logic Array ). Processor 301 may also include a main processor, which is a processor for processing data in an awake state, also referred to as a CPU (Central ProcessingUnit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. The processor 301 may also include an AI (ARTIFICIAL INTELLIGENCE ) processor for processing related chassis detection based parking detection operations so that the chassis detection based parking detection model may be trained and learned autonomously, improving efficiency and accuracy.
Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 801 to implement the chassis detection based parking detection method provided by the method embodiments of the present application.
In some embodiments, the terminal may further optionally include: a communication interface 303, and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. The respective peripheral devices may be connected to the communication interface 303 through a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power supply 306.
The communication interface 303 may be used to connect at least one peripheral device associated with an I/O (Input/Output) to the processor 301 and the memory 302. The communication interface 303 is used to receive the movement tracks of the plurality of mobile terminals and other data uploaded by the user through the peripheral device. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 301, the memory 302, and the communication interface 303 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 304 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals, so that movement trajectories and other data of a plurality of mobile terminals can be acquired. The radio frequency circuit 304 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 304 includes: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuitry 304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (WIRELESS FIDELITY ) networks. In some embodiments, the radio frequency circuit 304 may further include NFC (NEAR FIELD Communication) related circuits, which is not limited by the present application.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display 305 is a touch screen, the display 305 also has the ability to collect touch signals at or above the surface of the display 305. The touch signal may be input as a control signal to the processor 301 for processing. At this point, the display 305 may also be used to provide virtual buttons and/or virtual keyboards, also referred to as soft buttons and/or soft keyboards. In some embodiments, the display 305 may be one, the front panel of an electronic device; in other embodiments, the display screen 305 may be at least two, respectively disposed on different surfaces of the electronic device or in a folded design; in still other embodiments, the display 305 may be a flexible display disposed on a curved surface or a folded surface of the electronic device. Even more, the display screen 305 may be arranged in an irregular pattern other than rectangular, i.e., a shaped screen. The display 305 may be made of LCD (LiquidCrystal Display ), OLED (Organic Light-Emitting Diode) or other materials.
The power supply 306 is used to power the various components in the electronic device. The power source 306 may be alternating current, direct current, disposable or rechargeable. When the power source 306 comprises a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of a chassis-based detection parking detection device and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a parking detection method based on chassis detection.
In this embodiment, the parking detection method based on chassis detection includes the following steps:
Step S100, acquiring an original image data set of a vehicle body frame, and marking the chassis position in the original image data set to acquire a training image data set of the vehicle body frame.
Specifically, in order to avoid the problem of inaccurate detection of the vehicle position caused by the perspective relationship of the camera, the embodiment constructs a chassis detection model, and identifies the position of the chassis through the vehicle body frame, thereby determining the parking event of the vehicle.
In practical application, an original image data set of a vehicle body frame is firstly obtained, the characteristics of the vehicle body frame in the original image data set are extracted, and the positions of chassis corresponding to the characteristics of the vehicle body frame are marked; and building a training image data set of the vehicle body frame by utilizing the characteristics of the vehicle body frame and the positions of the corresponding chassis.
It is easy to understand that in the process of building the training image data set of the vehicle body frame, the original image data set of the vehicle body frame is firstly obtained, after the characteristics of the vehicle body frame are extracted, the chassis positions corresponding to the characteristics of the vehicle body frame are marked, the training image data set is obtained, and further the training of the chassis detection model is carried out.
And step S200, inputting the training image data set into a neural network model for training to obtain a chassis detection model.
Specifically, after training image data is obtained, the training image data is input into a neural network model for training to obtain a chassis detection model, so that when the vehicle body frame image data is received, the vehicle body frame image data can be identified by using the chassis detection model, and the position information of the vehicle chassis in the vehicle body frame is obtained.
Further, the characteristics of the vehicle body frame and the positions of the corresponding chassis can be input into a neural network model for training to obtain a chassis detection model; the neural network model is a deep learning regression model.
And step S300, when a target car body frame image is received, detecting the position of a chassis in the target car body frame image by using the chassis detection model, and obtaining the vertex position information of the chassis in the target car body frame image.
Specifically, after the trained chassis detection model is obtained, it may be monitored whether the target body frame image is acquired.
It is easy to understand that when a test image data set is received, whether the stop movement time of a target vehicle in the test image data set exceeds a preset value is judged, and if so, a target vehicle body frame image is determined; and detecting the chassis position in the target car body frame image by using the chassis detection model, and obtaining the vertex position information of the chassis in the target car body frame image.
Furthermore, before the chassis detection model detects the chassis position, parking lot monitoring data is also required to be acquired, and whether the image data in the parking lot monitoring data is an original image data set of the vehicle body frame is judged.
Specifically, a monitoring video is obtained; wherein, the monitoring video records frame images of the target parking space; and judging whether the frame image has the characteristics of the vehicle body frame or not, if so, extracting the frame image with the characteristics of the vehicle body frame to obtain a test image data set of the vehicle body frame.
Step S400, obtaining standard position information of the target parking space, and determining parking information of the vehicle based on the vertex position information and the standard position information.
Specifically, when the vertex position information of the chassis in the target body frame image is acquired, standard position information of the target parking space needs to be acquired, and then parking information of the vehicle is determined according to the standard position information and the vertex position information.
Further, standard position information of the target parking space is obtained; the standard position information comprises the vertex position and/or the boundary position of the target parking space; and monitoring whether the vertex position information meets the vertex position parking condition or the boundary position parking condition, and if so, determining the parking information of the vehicle.
In practical application, the vertex position parking condition is that the sum of the distances between the vertex position of the chassis in the target vehicle body frame image and the vertex position corresponding to the target parking space is smaller than a first preset value; and the boundary position parking condition is that the sum of the distance between the vertex position of the chassis in the target car body frame image and the boundary position corresponding to the target parking space is smaller than a second preset value.
It should be noted that, the first preset value and the second preset value may be specifically set according to actual situations, which is not limited in this embodiment.
In the embodiment, the chassis position of the original image dataset of the vehicle body frame is marked, and then the chassis detection model is trained and obtained, so that the vertex position information of the chassis is obtained when the vehicle body frame image is detected, the parking information of the vehicle is determined according to the vertex position information and the standard position information of the parking space, the position of the chassis relative to the vehicle body frame is subjected to deep learning and recognition, the parking information of the vehicle is determined, the problem of inaccurate vehicle position detection caused by the perspective relation of a camera is avoided, and the accuracy of a parking detection event is improved.
Referring to fig. 3, fig. 3 is a block diagram illustrating a structure of an embodiment of a parking detection device based on chassis detection according to the present invention.
As shown in fig. 3, a parking detection device based on chassis detection according to an embodiment of the present invention includes:
the marking module 10 is used for acquiring an original image data set of the vehicle body frame, marking the chassis position in the original image data set and acquiring a training image data set of the vehicle body frame;
The training module 20 is configured to input the training image dataset into a neural network model for training, so as to obtain a chassis detection model;
The detection module 30 is configured to detect a chassis position in the target vehicle body frame image by using the chassis detection model when the target vehicle body frame image is received, so as to obtain vertex position information of a chassis in the target vehicle body frame image;
The determining module 40 is configured to obtain standard position information of the target parking space, and determine parking information of the vehicle based on the vertex position information and the standard position information.
As one embodiment, the marking module 10 is further configured to acquire an original image dataset of a vehicle body frame, extract the features of the vehicle body frame in the original image dataset, and mark the positions of chassis corresponding to the features of the vehicle body frame; and building a training image data set of the vehicle body frame by utilizing the characteristics of the vehicle body frame and the positions of the corresponding chassis.
As an embodiment, the training module 20 is further configured to input the features of the vehicle body frame and the positions of the chassis corresponding to the features of the vehicle body frame into a neural network model for training, so as to obtain a chassis detection model; the neural network model is a deep learning regression model.
As an implementation manner, the parking detection device based on chassis detection further comprises a judging module 50, and the judging module 50 is further used for acquiring a monitoring video; wherein, the monitoring video records frame images of the target parking space; and judging whether the frame image has the characteristics of the vehicle body frame or not, if so, extracting the frame image with the characteristics of the vehicle body frame to obtain a test image data set of the vehicle body frame.
In one embodiment, the detection module 30 is further configured to, when receiving the test image data set, determine whether a stop movement time of the target vehicle in the test image data set exceeds a preset value, and if so, determine a target vehicle body frame image; and detecting the chassis position in the target car body frame image by using the chassis detection model, and obtaining the vertex position information of the chassis in the target car body frame image.
As one embodiment, the determining module 40 is further configured to obtain standard location information of the target parking space; the standard position information comprises the vertex position and/or the boundary position of the target parking space; and monitoring whether the vertex position information meets the vertex position parking condition or the boundary position parking condition, and if so, determining the parking information of the vehicle.
As one embodiment, in the determining module 40, the vertex position parking condition is that a sum of distances between a vertex position of the chassis in the target vehicle body frame image and a vertex position corresponding to the target parking space is smaller than a first preset value; and the boundary position parking condition is that the sum of the distance between the vertex position of the chassis in the target car body frame image and the boundary position corresponding to the target parking space is smaller than a second preset value.
According to the parking detection device based on chassis detection, the chassis position of the original image dataset of the vehicle body frame is marked, the chassis detection model is further trained and obtained, so that the vertex position information of the chassis is obtained when the vehicle body frame image is detected, the parking information of a vehicle is determined according to the vertex position information and the standard position information of a parking space, the position of the chassis relative to the vehicle body frame is subjected to deep learning and recognition, the parking information of the vehicle is further determined, the problem of inaccurate vehicle position detection caused by camera perspective relation is avoided, and the accuracy of a parking detection event is improved.
Other embodiments or specific implementation manners of the parking detection device based on chassis detection according to the present invention may refer to the above method embodiments, and will not be described herein.
In addition, the embodiment of the application also provides a storage medium, wherein the storage medium stores a parking detection program based on chassis detection, and the parking detection program based on chassis detection realizes the steps of the parking detection method based on chassis detection. Therefore, a detailed description will not be given here. In addition, the description of the beneficial effects of the same method is omitted. For technical details not disclosed in the embodiments of the computer-readable storage medium according to the present application, please refer to the description of the method embodiments of the present application. As an example, the program instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a computer-readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
It should be further noted that the above-described apparatus embodiments are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present invention may be implemented by means of software plus necessary general purpose hardware, or of course by means of special purpose hardware including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. But a software program implementation is a preferred embodiment for many more of the cases of the present invention. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a Read-only memory (ROM), a random-access memory (RAM, randomAccessMemory), a magnetic disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
Claims (10)
1. A method of detecting a parking based on chassis detection, the method comprising the steps of:
Acquiring an original image data set of a vehicle body frame, and marking the chassis position in the original image data set to obtain a training image data set of the vehicle body frame;
Inputting the training image data set into a neural network model for training to obtain a chassis detection model;
when a target car body frame image is received, detecting the position of a chassis in the target car body frame image by using the chassis detection model, and obtaining vertex position information of the chassis in the target car body frame image;
And acquiring standard position information of the target parking space, and determining parking information of the vehicle based on the vertex position information and the standard position information.
2. The method for detecting a parking based on chassis detection according to claim 1, wherein the step of acquiring an original image dataset of a vehicle body frame and marking a chassis position in the original image dataset to obtain a training image dataset of the vehicle body frame comprises the steps of:
acquiring an original image data set of a vehicle body frame, extracting the characteristics of the vehicle body frame in the original image data set, and marking the position of a chassis corresponding to the characteristics of the vehicle body frame;
And building a training image data set of the vehicle body frame by utilizing the characteristics of the vehicle body frame and the positions of the corresponding chassis.
3. The parking detection method based on chassis detection according to claim 1, wherein the step of inputting the training image dataset into a neural network model for training to obtain a chassis detection model specifically comprises:
inputting the characteristics of the vehicle body frame and the positions of the corresponding chassis into a neural network model for training to obtain a chassis detection model; the neural network model is a deep learning regression model.
4. The chassis detection-based parking detection method according to claim 1, wherein the method further comprises:
Acquiring a monitoring video; wherein, the monitoring video records frame images of the target parking space;
And judging whether the frame image has the characteristics of the vehicle body frame or not, if so, extracting the frame image with the characteristics of the vehicle body frame to obtain a test image data set of the vehicle body frame.
5. The method for detecting a parking based on chassis detection according to claim 4, wherein the step of detecting a chassis position in the target frame image by using the chassis detection model when the target frame image is received, and obtaining vertex position information of the chassis in the target frame image, specifically comprises:
When a test image data set is received, judging whether the stop moving time of a target vehicle in the test image data set exceeds a preset value, and if so, determining a target vehicle body frame image;
And detecting the chassis position in the target car body frame image by using the chassis detection model, and obtaining the vertex position information of the chassis in the target car body frame image.
6. The method for detecting a vehicle parking based on chassis detection according to claim 1, wherein the step of acquiring the standard position information of the target parking space and determining the vehicle parking information based on the vertex position information and the standard position information comprises the steps of:
obtaining standard position information of a target parking space; the standard position information comprises the vertex position and/or the boundary position of the target parking space;
And monitoring whether the vertex position information meets the vertex position parking condition or the boundary position parking condition, and if so, determining the parking information of the vehicle.
7. The chassis detection-based parking detection method according to claim 6, wherein the vertex position parking condition is that a sum of distances between a vertex position of a chassis in the target vehicle body frame image and a vertex position corresponding to a target parking space is smaller than a first preset value; and the boundary position parking condition is that the sum of the distance between the vertex position of the chassis in the target car body frame image and the boundary position corresponding to the target parking space is smaller than a second preset value.
8. A chassis detection-based parking detection device, characterized in that the chassis detection-based parking detection device comprises:
The marking module is used for acquiring an original image data set of the vehicle body frame, marking the chassis position in the original image data set and acquiring a training image data set of the vehicle body frame;
The training module is used for inputting the training image data set into a neural network model for training to obtain a chassis detection model;
the detection module is used for detecting the chassis position in the target car body frame image by using the chassis detection model when the target car body frame image is received, and obtaining the vertex position information of the chassis in the target car body frame image;
and the determining module is used for acquiring the standard position information of the target parking space and determining the parking information of the vehicle based on the vertex position information and the standard position information.
9. A chassis detection-based parking detection apparatus, characterized in that the chassis detection-based parking detection apparatus comprises: a memory, a processor and a chassis detection based parking detection program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the chassis detection based parking detection method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a chassis detection based parking detection program which when executed by a processor implements the steps of the chassis detection based parking detection method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111577660.2A CN114494795B (en) | 2021-12-22 | 2021-12-22 | Parking detection method, device, equipment and storage medium based on chassis detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111577660.2A CN114494795B (en) | 2021-12-22 | 2021-12-22 | Parking detection method, device, equipment and storage medium based on chassis detection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114494795A CN114494795A (en) | 2022-05-13 |
| CN114494795B true CN114494795B (en) | 2024-10-15 |
Family
ID=81493174
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111577660.2A Active CN114494795B (en) | 2021-12-22 | 2021-12-22 | Parking detection method, device, equipment and storage medium based on chassis detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114494795B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115359650B (en) * | 2022-07-06 | 2024-10-25 | 浙江大华技术股份有限公司 | Parking berth detection method, device, computer equipment and storage medium |
| CN119360659A (en) * | 2024-10-23 | 2025-01-24 | 北京卓视智通科技有限责任公司 | Vehicle chassis position positioning method, device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321351A (en) * | 2015-11-03 | 2016-02-10 | 徐承柬 | Parking space arrearage management method and system |
| CN111784857A (en) * | 2020-06-22 | 2020-10-16 | 浙江大华技术股份有限公司 | Parking space management method and device and computer storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6761708B2 (en) * | 2016-09-05 | 2020-09-30 | パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ | Parking position identification method, parking position learning method, parking position identification system, parking position learning device and program |
| CN111476169B (en) * | 2020-04-08 | 2023-11-07 | 智慧互通科技股份有限公司 | Complex scene road side parking behavior identification method based on video frame |
| CN113874927B (en) * | 2020-04-30 | 2023-07-18 | 京东方科技集团股份有限公司 | Parking detection method, system, processing device and storage medium |
-
2021
- 2021-12-22 CN CN202111577660.2A patent/CN114494795B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321351A (en) * | 2015-11-03 | 2016-02-10 | 徐承柬 | Parking space arrearage management method and system |
| CN111784857A (en) * | 2020-06-22 | 2020-10-16 | 浙江大华技术股份有限公司 | Parking space management method and device and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114494795A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114494795B (en) | Parking detection method, device, equipment and storage medium based on chassis detection | |
| US10599913B2 (en) | Face model matrix training method and apparatus, and storage medium | |
| CN114610911B (en) | Multi-modal knowledge intrinsic representation learning method, device, equipment and storage medium | |
| CN113761255B (en) | Robot indoor positioning method, device, equipment and storage medium | |
| CN114048168A (en) | Electronic archive filing method, device, equipment, system and storage medium | |
| CN113225234B (en) | Asset detection method, apparatus, terminal device, and computer-readable storage medium | |
| CN114926806B (en) | Lane detection method, device, equipment and storage medium based on deep learning | |
| CN113269730B (en) | Image processing method, image processing device, computer equipment and storage medium | |
| CN113824929B (en) | Method and device for monitoring abnormal behaviors of parking lot and parking lot monitoring system | |
| CN112183217B (en) | Gesture recognition method, interaction method based on gesture recognition and mixed reality glasses | |
| CN114926807B (en) | Vehicle event recognition method, device, equipment and storage medium | |
| CN111738034B (en) | Lane line detection method and device | |
| CN114168962B (en) | Method, device, terminal device and storage medium for obtaining vulnerability data | |
| CN114115013B (en) | Control method of robot motor, terminal device and storage medium | |
| CN114024718B (en) | Malicious domain name detection method, device, equipment and storage medium | |
| CN114511824B (en) | Method, device, equipment and storage medium for detecting quality of images taken of illegal vehicles | |
| CN114494412B (en) | Indoor monitoring method, device, system and storage medium | |
| CN114973055A (en) | Vehicle motion state detection method, device, device and storage medium | |
| CN114648745B (en) | Road detection method, device, equipment and storage medium based on deep learning | |
| CN114862000A (en) | Trajectory prediction method, apparatus, device and storage medium | |
| CN113282925A (en) | Malicious file detection method and device, terminal equipment and storage medium | |
| CN113741215B (en) | Equipment control method and device based on associated characteristic data | |
| CN113888669B (en) | Winding layout drawing method, device, equipment and storage medium | |
| CN113778063B (en) | Equipment control method and device based on longitudinal characteristic data | |
| CN114237513B (en) | File data storage, writing method, reading method and storage system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |