CN111199200A - Wearing detection method and device based on electric protection equipment and computer equipment - Google Patents
Wearing detection method and device based on electric protection equipment and computer equipment Download PDFInfo
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Abstract
本申请涉及一种基于电力防护装备的佩戴检测方法、装置和计算机设备。所述方法包括：获取监控设备采集的图像数据，所述图像数据包括人体图像；调用预设的人体识别模型，将所述人体图像输入所述人体识别模型，得到多个区域类型；根据所述区域类型对所述人体图像进行分割，得到对应的目标区域图像；获取所述区域类型对应的标准装备信息，基于所述标准装备信息对所述目标区域图像进行佩戴检测；当佩戴检测结果为未佩戴装备时，生成告警提示信息。采用本方法能够避免传统人工检测的错检和漏检，有效的提高对电力防护装备的佩戴检测的准确性。
The present application relates to a wear detection method, device and computer equipment based on electrical protective equipment. The method includes: acquiring image data collected by a monitoring device, the image data including a human body image; calling a preset human body recognition model, and inputting the human body image into the human body recognition model to obtain multiple area types; Segment the human body image by region type to obtain the corresponding target area image; obtain the standard equipment information corresponding to the area type, and perform wearing detection on the target area image based on the standard equipment information; when the wearing detection result is no When wearing the equipment, an alarm prompt message is generated. Using the method can avoid false detection and missed detection of traditional manual detection, and effectively improve the accuracy of wearing detection of power protective equipment.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a wear detection method and apparatus based on power protection equipment, a computer device, and a storage medium.
Background
In order to build electric power engineering, a large number of constructors respectively carry out electric power construction in various different electric power construction environments, and the safety of the constructors in the electric power construction process is particularly important. Therefore, various electric power protective equipment is designed aiming at various different electric power construction environments. Constructors should wear corresponding electric protection equipment in the electric construction process to protect the safety of the constructors. For example, electromagnetic shielding clothing is worn in construction environments where electromagnetic radiation is large.
However, in the power construction process, the power protection equipment corresponding to the power construction environment is often not worn by constructors, so that certain potential safety hazards exist. Therefore, it is necessary to detect whether or not the power protection equipment is worn by the constructor. In the conventional manner, the wearing detection is usually performed manually. However, the time and the energy of the manual work are limited, the missed detection and the false detection are easy to occur, and the wearing detection accuracy of the electric protection equipment is low.
Disclosure of Invention
In view of the above, it is necessary to provide a wearing detection method, an apparatus, a computer device and a storage medium based on electric protection equipment, which can improve the wearing detection accuracy of the electric protection equipment, in order to solve the technical problem of low wearing detection accuracy.
A power protective equipment-based wear detection method, the method comprising:
acquiring image data acquired by monitoring equipment, wherein the image data comprises a human body image;
calling a preset human body recognition model, and inputting the human body image into the human body recognition model to obtain a plurality of region types;
segmenting the human body image according to the region type to obtain a corresponding target region image;
acquiring standard equipment information corresponding to the area type, and carrying out wearing detection on the target area image based on the standard equipment information;
and when the wearing detection result indicates that the equipment is not worn, generating alarm prompt information.
In one embodiment, the obtaining of the standard equipment information corresponding to the area type includes:
acquiring a monitoring device identifier corresponding to the monitoring device;
determining corresponding monitoring information according to the monitoring equipment identifier;
and acquiring a standard equipment list corresponding to the monitoring information, wherein the standard equipment list comprises the area type and standard equipment information corresponding to the area type.
In one embodiment, the target area image includes a face image, and the acquiring standard equipment information corresponding to the area type includes:
carrying out face recognition on the face image to obtain face features;
determining a corresponding personnel identifier according to the human face characteristics, and acquiring work order information corresponding to the personnel identifier;
and acquiring standard equipment information corresponding to the area types according to the work order information.
In one embodiment, the wear detection of the target area image based on the standard equipment information includes:
calling a wearing detection model corresponding to the region type;
inputting the target area image into the wearing detection model, and carrying out wearing detection based on the standard equipment information;
and acquiring a wearing detection result output by the wearing detection model.
In one embodiment, the generating the warning prompt message when the wearing detection result is that the equipment is not worn includes:
determining a target alarm strategy based on the monitoring equipment identification and the wearing detection result of the monitoring equipment;
and generating the alarm prompt information according to the target alarm strategy, and sending the alarm prompt information to corresponding alarm equipment.
A power protective equipment-based wear detection apparatus, the apparatus comprising:
the monitoring device comprises an image acquisition module, a monitoring module and a monitoring module, wherein the image acquisition module is used for acquiring image data acquired by the monitoring device, and the image data comprises a human body image;
the human body recognition module is used for calling a preset human body recognition model, inputting the human body image into the human body recognition model and obtaining a plurality of region types;
the image segmentation module is used for segmenting the human body image according to the region type to obtain a corresponding target region image;
the wearing detection module is used for acquiring standard equipment information corresponding to the area type and carrying out wearing detection on the target area image based on the standard equipment information;
and the warning prompt module is used for generating warning prompt information when the wearing detection result indicates that the equipment is not worn.
In one embodiment, the wearing detection module is further configured to obtain a monitoring device identifier corresponding to the monitoring device; determining corresponding monitoring information according to the monitoring equipment identifier; and acquiring a standard equipment list corresponding to the monitoring information, wherein the standard equipment list comprises the area type and standard equipment information corresponding to the area type.
In one embodiment, the wearing detection module is further configured to perform face recognition on the face image to obtain a face feature; determining a corresponding personnel identifier according to the human face characteristics, and acquiring work order information corresponding to the personnel identifier; and acquiring standard equipment information corresponding to the area types according to the work order information.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above power protection equipment-based wear detection method when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned power protection equipment-based wear detection method.
According to the wearing detection method and device based on the electric protection equipment, the computer equipment and the storage medium, image data collected by the monitoring equipment are obtained, the image data comprise human body images, the preset human body recognition model is called to recognize the human body images to obtain a plurality of area types, the human body images are segmented according to the area types, and target area images corresponding to a plurality of parts of the human body are obtained. The standard equipment information corresponding to the area type is acquired, the target area image is worn and detected based on the standard equipment information, whether the electric protection equipment is worn at a plurality of positions corresponding to constructors or not is accurately detected, the false detection and the missing detection of the traditional manual detection are avoided, and the wearing detection accuracy of the electric protection equipment is effectively improved. When the wearing detection result indicates that the equipment is not worn, alarm prompt information is generated, so that constructors are prompted to wear the electric protection equipment in time, and potential safety hazards in the electric construction process are reduced.
Drawings
FIG. 1 is a diagram of an application environment of a power protection equipment-based wear detection method in one embodiment;
FIG. 2 is a schematic flow chart of a power protection equipment-based wear detection method in one embodiment;
FIG. 3 is a flowchart illustrating the step of obtaining standard equipment information corresponding to the area type in one embodiment;
FIG. 4 is a block diagram of a power protection equipment based wear detection device in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The wearing detection method based on the electric protection equipment can be applied to the application environment shown in fig. 1. Wherein the monitoring device 102 may communicate with the server 104 via a network, and the server 104 may communicate with the alerting device 106 via a network. The server 104 acquires image data acquired by the monitoring device 102, wherein the image data includes a human body image. The server 104 calls a preset human body recognition model, and human body images are input into the human body recognition model to obtain a plurality of region types; and segmenting the human body image according to the region type to obtain a corresponding target region image. The server 104 acquires standard equipment information corresponding to the area type, and performs wearing detection on the target area image based on the standard equipment information. When the wearing detection result indicates that the equipment is not worn, the server 104 generates alarm prompt information and sends the alarm prompt information to the alarm device 106. And the alarm device 106 carries out alarm prompt according to the alarm prompt information. The monitoring device 102 may include, but is not limited to, various video capture devices and image capture devices, the server 104 may be implemented by an independent server or a server cluster composed of a plurality of servers, and the alerting device 106 may include, but is not limited to, various broadcast devices, personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
In one embodiment, as shown in fig. 2, there is provided a wear detection method based on power protection equipment, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
The server may correspond to multiple types of monitoring devices. For example, the monitoring device may be a camera or the like fixedly installed in the electric power construction area in advance for collecting video data or image data of the fixed electric power construction area. The monitoring equipment can also be movable, such as but not limited to a movable robot, a monitoring vehicle and the like, and can acquire video data or image data of the power construction area from multiple angles. The image data includes a human body image corresponding to the constructor. The image data including the human body image can be acquired by the monitoring equipment when the monitoring equipment detects the constructor, and can also be acquired by the server according to the video data acquired by the monitoring equipment.
The server may specifically obtain image data acquired by the monitoring device according to a preset frequency. Specifically, the server may acquire video data or image data of the electric power construction area corresponding to the monitoring device. When the server acquires the video data acquired by the monitoring equipment, the server can read a plurality of frames of images in the video data and extract the image data from the video data according to the preset frequency. When the server acquires the image data acquired by the monitoring device, the monitoring device may acquire the image data according to a preset frequency, the server may acquire each frame of image data acquired by the monitoring device, or the server may acquire the image data acquired by the monitoring device according to the preset frequency. The preset frequency may be a constant, and specifically may be preset by a user according to actual requirements, for example, the server may acquire one frame of image data every second. The preset frequency may also be a variable. For example, the preset frequency may be changed according to the change of time, and the frequency corresponding to the construction time is larger than the frequency corresponding to the non-construction time. The preset frequency may also vary with the number of workers in the monitored area. When no constructor exists in the monitoring area, the frequency of collecting image data is reduced, unnecessary detection of the image data without constructor is avoided, and the operation resource of the server is saved.
And step 204, calling a preset human body recognition model, and inputting a human body image into the human body recognition model to obtain a plurality of region types.
The server can call a preset human body recognition model, and the human body recognition model can be pre-established and obtained after sample training. The human body recognition model can be configured in the server after being trained, so that the server calls the human body recognition model to perform human body recognition on the human body image in the image data. The human detection model may be one of a variety of algorithmic models. For example, the human body recognition model may specifically be an SVM (Support Vector Machine) model, a neural network model, and the like. The neural network model may include at least one of a vgg (visual Geometry Group network) model, a fast R-CNN model, an ssd (single Shot multi box detector) model, a BP neural network model, and a YOLO model.
The server can input the human body image into the human body recognition model, and perform human body recognition operation on the human body image according to the human body recognition model to obtain a plurality of image areas output by the human body recognition model and area types respectively corresponding to the plurality of image areas. For example, the plurality of region types output by the human body recognition model may specifically include at least one of a head region, a hand region, an arm region, a torso region, a leg region, a foot region, and the like, so as to recognize a human body part corresponding to a constructor in the human body image. Each image region may be a rectangular region, or may be a region range of other shapes, and the image region may specifically include a position and a range of a corresponding type of body part in the human body image.
In one embodiment, the server may obtain training samples, and train the human recognition model according to the training samples. The training samples can comprise human body sample images respectively corresponding to the constructors in various electric power construction environments. For example, a power construction environment may specifically include construction for indoor ground power equipment, construction for outdoor ground power equipment, construction for underground power equipment, and construction for overhead power equipment. The outdoor ground construction environment and the high-altitude construction environment can also correspond to various meteorological environments, the illumination intensity and the like corresponding to different meteorological environments are different, and the training samples can comprise sample images corresponding to different meteorological environments. The server can obtain human body labeling information corresponding to the sample image, wherein the human body labeling information specifically comprises a plurality of image areas corresponding to the human body image in the sample image and area types respectively corresponding to the image areas. The server can train the established standard human body recognition model based on the sample image and the human body labeling information to obtain a successfully trained target human body recognition model, and the target human body recognition model is configured in the server.
And step 206, segmenting the human body image according to the region type to obtain a corresponding target region image.
The server can segment the human body image according to a plurality of image areas output by the human body recognition model and area types respectively corresponding to the image areas. Specifically, the server may determine a corresponding position in the human body image according to image areas corresponding to the respective multiple area types output by the human body recognition model, and perform respective capturing according to ranges corresponding to the multiple image areas, so as to segment the human body image, and obtain multiple target area images corresponding to the multiple image areas. When the image data includes a plurality of human body images, the human body recognition model can perform human body recognition on the plurality of human body images, and the target area images corresponding to the plurality of human body images are obtained by segmentation. The target area image may specifically include at least one of a head image, a hand image, an arm image, a torso image, a leg image, a foot image, and the like corresponding to the constructor, corresponding to the area type of the image area.
And step 208, acquiring standard equipment information corresponding to the area type, and carrying out wearing detection on the target area image based on the standard equipment information.
The server can obtain standard equipment information corresponding to the area type. Specifically, the server may preset and store an association relationship between the area type and the standard equipment information. The server may acquire the standard equipment information associated with the area type according to the association relationship between the area type and the standard equipment information. The standard equipment information is equipment information corresponding to power protection equipment to be worn by a constructor in a power construction environment. The electric protection equipment is equipment which can protect constructors in the electric construction process and has a protection effect. For example, the electric heatstroke prevention equipment may specifically include safety helmets, electromagnetic shielding clothes, insulating gloves, safety ropes, insulating boots, and the like. The standard equipment information corresponding to the electric power construction environment may specifically include at least one of a plurality of kinds of electric power protection equipment information, such as helmet information, electromagnetic shielding clothing information, insulating glove information, safety rope information, and insulating boot information.
According to the wearing rule corresponding to the electric protection equipment, different area types can correspond to different standard equipment information. For example, the head region may be associated with headgear information, the hand region may be associated with insulating glove information, and the torso region may be associated with electromagnetic shield suit information. In one embodiment, the region types may correspond to different standard equipment information under different power construction environments. For example, in a high-altitude construction environment, the torso region may correspond to safety line information. Whereas in an indoor ground construction environment, the torso region may not be associated with safety line information. The electromagnetic shielding clothes need to be worn in an electric power construction environment with large electromagnetic radiation, and the electromagnetic shielding clothes do not need to be worn in an electric power construction environment with small electromagnetic radiation. The user can set the incidence relation between the area type and the standard equipment information in different power construction environments in advance.
The server can wear and detect the target area image based on the standard equipment information corresponding to the area type, and detect whether the constructor in the target area image wears the electric protection equipment corresponding to the standard equipment information. Specifically, the server may invoke a wear detection model, the wear detection model may be pre-trained and configured in the server, and the wear detection model may include at least one of a plurality of target detection algorithms. The server can input the target area image into the wearing detection model, and the wearing detection model is used for carrying out wearing detection based on the standard equipment information to obtain a wearing detection result output by the wearing detection model. In one embodiment, the wearing detection model may correspond to the area type, and the server may invoke a different wearing detection model corresponding to the area type for wearing detection according to a different area type corresponding to the image area image. The target detection algorithms of the wearing detection models corresponding to different region types may be the same or different. The server can also call a complete wearing detection model, the wearing detection model can comprise a plurality of detection sub-networks corresponding to the area types, and the server can detect the target area image according to the detection sub-networks corresponding to the area types in the wearing detection model to obtain a wearing detection result.
And step 210, generating alarm prompt information when the wearing detection result is that the equipment is not worn.
The server carries out wearing detection on the target area image, and the obtained wearing detection result can comprise one of worn equipment and unworn equipment. When the wearing detection result is that the equipment is worn, the detection is determined to be successful, the server can repeatedly acquire image data acquired by the monitoring equipment, and whether the corresponding electric protection equipment is worn by constructors in the electric construction process or not is continuously detected. When the wearing detection result is that the equipment is not worn, the detection is determined to be failed, and the server can generate alarm prompt information according to the wearing detection result. The alert prompt message may be at least one of a plurality of message types. For example, the warning prompt message may be a text prompt message, a voice prompt message, or a combination of the text prompt message and the voice prompt message. The server can send the generated warning prompt information to the warning equipment corresponding to the monitoring equipment, so that the warning equipment displays the warning prompt information to prompt constructors to wear the electric power protection equipment.
In this embodiment, the server may obtain image data collected by the monitoring device, where the image data includes a human body image. The server can call a preset human body recognition model, perform human body recognition on the human body image according to the human body recognition model to obtain area types corresponding to the image areas respectively, classify the human body image according to the area types to obtain a target area image, and perform wearing detection on the target area image based on standard equipment information corresponding to the area types. The server carries out human body identification to the human body image, refines the human body image, and whether accurate a plurality of positions that correspond constructor wear electric protection equipment and detect has avoided the false retrieval and the hourglass of traditional artifical detection to detect, and the effectual accuracy that detects of wearing that has improved electric protection equipment. When the wearing detection result indicates that the equipment is not worn, alarm prompt information is generated, so that constructors are prompted to wear the electric protection equipment in time, and potential safety hazards in the electric construction process are reduced.
In an embodiment, as shown in fig. 3, the step of acquiring standard equipment information corresponding to the area type includes:
And step 304, determining corresponding monitoring information according to the monitoring equipment identifier.
When the server acquires the image data acquired by the monitoring equipment, the server can also acquire the monitoring equipment identifier corresponding to the monitoring equipment. The monitoring device identification may be used to uniquely tag the monitoring device. The monitoring device identification may be at least one of a plurality of unique identification information corresponding to the monitoring device. For example, the monitoring device identifier may be a monitoring device number corresponding to the monitoring device. The server may obtain monitoring information corresponding to the monitoring device according to the monitoring device identifier, where the monitoring information may include information of a monitoring area corresponding to the monitoring device. The monitoring area corresponding to the monitoring equipment corresponds to the electric power construction environment of constructors. The server may obtain a standard equipment list corresponding to the monitoring information according to the monitoring information corresponding to the monitoring device. In one embodiment, the monitoring information includes a standard equipment list, which may be preset and stored in association with the monitoring device identification. The standard equipment list is an electric protection equipment list which needs to be worn by constructors in an electric construction environment corresponding to the monitored area. The standard equipment list records the association relationship between the area type and the standard equipment information, and specifically may include information on electric protection equipment to be worn by a plurality of body parts of a constructor in a corresponding monitoring area. The server may record the association relationship between the area type and the standard equipment information in the form of a data table, and may also record the association relationship in other forms. The server may read standard equipment information corresponding to the area type from the standard equipment list.
In this embodiment, the server may obtain a monitoring device identifier corresponding to the monitoring device, and determine corresponding monitoring information according to the monitoring device identifier. The server may acquire a standard equipment list corresponding to the monitoring information, the standard equipment list including a plurality of area types and standard equipment information corresponding to the area types. The server can read standard equipment information corresponding to the area type from the standard equipment list, the standard equipment information corresponds to the real electric power construction environment, and electric power protection equipment worn by constructors in the electric power construction environment is accurately recorded. Wear the detection based on standard equipment information, the effectual accuracy that improves and wear the detection.
In one embodiment, the target area image includes a face image, and the step of acquiring standard equipment information corresponding to the area type includes: carrying out face recognition on the face image to obtain face features; determining a corresponding personnel identifier according to the face characteristics, and acquiring work order information corresponding to the personnel identifier; and acquiring standard equipment information corresponding to the plurality of area types according to the work order information.
The target area image is image data segmented by the server according to the human body image corresponding to the constructor, and the target area image specifically may include a face image corresponding to the head area of the constructor. The server can call the face recognition model, and the face recognition model is used for carrying out face recognition on the face image to obtain the face features in the face image output after the face recognition model is operated. The face recognition model can be obtained by the server through training a large number of face images and corresponding face feature labeling data. The face recognition model can perform feature extraction on the face image based on a face recognition algorithm. The face recognition algorithm may be one of a variety of recognition algorithms. For example, the face recognition algorithm may be a recognition algorithm based on face feature points, an algorithm for performing recognition using a neural network, or an algorithm for performing recognition using a support vector machine.
Specifically, the server may compare the face features corresponding to the face image with face information in a preset image set to obtain feature similarity. The preset image set refers to an image set which is obtained in advance by a server and stored in a corresponding database, the preset image set comprises uploaded face images of power construction personnel, and face information in the preset image set comprises face images, personnel identifications and face characteristics and the like corresponding to the face images. The server can compare the facial features corresponding to the facial images with the facial features of the plurality of facial images in the preset image set to obtain feature similarity between the facial features and the plurality of facial images. The server can compare the multiple feature similarities, and screen out the personnel identification corresponding to the face image with the highest similarity from the multiple feature similarities. The server can also compare the feature similarity with the highest similarity with a similarity threshold, and when the feature similarity is greater than the similarity threshold, the corresponding personnel identifier is screened out, and the human face features are further screened out, so that the accuracy of screening the personnel identifier is improved.
The server can obtain work order information corresponding to the staff identification, the work order information refers to construction work order information corresponding to constructors, and the work order information specifically comprises staff identification, work order numbers, construction area identification, construction tasks and other information corresponding to the constructors. The work order information may further include standard equipment information that the constructor should wear when performing the corresponding construction task, and a region type that the standard equipment should wear. The server may extract standard equipment information corresponding to each of the plurality of area types from the work order information. In one embodiment, the server may further obtain a corresponding standard equipment list according to the construction area identifier, and obtain standard equipment information corresponding to each of the plurality of area types from the standard equipment list.
In this embodiment, the server may perform face recognition on a face image in the head region image to obtain a face feature, and determine a corresponding person identifier according to the face feature. The server can obtain work order information corresponding to the personnel identification, and the work order information comprises construction tasks required to be executed by constructors and electric protection equipment required to be worn in the electric power construction process. The server can acquire the standard equipment information corresponding to the plurality of area types according to the work order information, and the wearing detection accuracy is effectively improved.
In one embodiment, the target area image may be a head image corresponding to a constructor, the standard equipment information corresponding to the head area is helmet information, and the server may perform wear detection on the head image based on the helmet information. Specifically, the server may obtain color codes corresponding to a plurality of pixel points in the head image, and the color codes may be used to identify colors of the corresponding pixel points. The color code corresponding to the pixel point may specifically be a color value corresponding to an RGB color channel, and may also be a hexadecimal color code corresponding to an RGB color value. The server can obtain standard color codes from the safety helmet information, the standard color codes can be color codes in a color code set, the color code set can specifically be color codes corresponding to safety helmets of multiple colors, and the color codes corresponding to the safety helmets in multiple power construction environments respectively are set. The server can compare the color code corresponding to the pixel point in the head image with the standard color code, and screen the pixel point in the head image according to the comparison result. Specifically, the server may compare the color code corresponding to the pixel point with the standard color code. And when the color code corresponding to the pixel point belongs to the standard color code, the server determines that the comparison is successful. And when the color code corresponding to the pixel point does not belong to the standard color code, the server determines that the comparison fails. The server can screen out the successfully-compared pixel points according to the comparison result, the pixel points with the successfully-compared color codes are recorded as target pixel points, and the target pixel points are the pixel points in the head image, wherein the color of the pixel points is consistent with that of the helmet.
The server can count the number of the target pixel points and determine the proportion of the target pixel points in the head image. And when the proportion corresponding to the target pixel point is larger than or equal to the pixel point threshold, determining that the detection result is that a safety helmet exists in the head image. And when the occupation ratio corresponding to the target pixel point is smaller than the pixel point threshold value, determining that the detection result is that no safety helmet exists in the head image. The pixel point threshold may be a minimum ratio of a target pixel point in the head image, which is obtained in advance according to statistics of a large number of head images at multiple angles. In one embodiment, when the proportion corresponding to the target pixel point is greater than or equal to the pixel point threshold, the server may further obtain the distribution condition of the target pixel point in the head image. When the target pixel points are distributed dispersedly, the detection result is determined that no safety helmet exists in the head image. When the target pixel points are distributed in a concentrated mode, the fact that safety helmets exist in the head images is determined as a detection result. The server detects according to the distribution condition of the target pixel points, avoids the influence of other objects with the same color in the head image on the detection result, and further improves the accuracy of the safety helmet detection. The server may determine the wearing detection result according to whether the helmet exists in the head image.
In the embodiment, the server wears the detection to the head image according to the safety helmet information, and the accuracy of the safety helmet wearing detection is effectively improved. When the wearing detection result indicates that the safety helmet is not worn, the server can generate alarm prompt information so as to prompt constructors to wear the safety helmet in time and reduce potential safety hazards in the power construction process.
In an embodiment, the step of generating the warning prompt message when the wearing detection result indicates that the equipment is not worn includes: determining a target alarm strategy based on a monitoring equipment identifier and a wearing detection result of the monitoring equipment; and generating alarm prompt information according to the target alarm strategy, and sending the alarm prompt information to corresponding alarm equipment.
The server can obtain a monitoring device identifier corresponding to the monitoring device, and determine a target alarm strategy according to the monitoring device identifier and the detection result. Different monitoring equipment can correspond different monitoring areas, and the construction risk degree of different monitoring areas is also different. The server can determine the danger level of the corresponding monitoring area according to the monitoring equipment identifier corresponding to the monitoring equipment, and determine a target alarm strategy according to the danger level corresponding to the monitoring area and the detection result of the unworn equipment. Wherein the target alarm policy is one of a plurality of preset alarm policies. The preset alarm strategy can be preset by a user according to actual application requirements and stored in the server, and comprises a danger level, an alarm device identifier needing linkage, an alarm prompt mode, an information type and the like. There may be an association between the alert device identification and the monitoring device identification. The server can match the monitoring equipment identification and the wearing detection result with a plurality of preset alarm strategies, and determine the matched preset alarm strategy as a target alarm strategy.
The target alarm policy includes an information type and an alarm device identifier, and the information type and the alarm device identifier may be corresponding. The server may generate corresponding warning prompt information according to the information type in the target warning policy, where the warning prompt information may specifically include which standard electric power protection equipment is not worn by a constructor corresponding to the human body image. The alert prompt message may be at least one of a plurality of message types. For example, the warning prompt message may be a text prompt message, a voice prompt message, or a combination of the text prompt message and the voice prompt message. The server can send the generated alarm prompt information to the alarm equipment marked by the alarm equipment identification corresponding to the information type, so that the alarm equipment displays the corresponding alarm prompt information. For example, the warning device can display text prompt information through a corresponding display interface and display sound prompt information through a loudspeaker, so that a constructor who does not wear the equipment can be prompted to wear corresponding electric power protection equipment in time.
In one embodiment, because the power construction environments are various and the coverage range is wide, more power construction areas need to be monitored safely, the operation pressure of wearing and detecting all monitoring areas by one server is high, and the detection delay is high. Therefore, the wearing detection method based on the electric protection equipment can be particularly applied to the edge server. The edge server acquires a human body image acquired by the monitoring equipment to perform human body identification, and performs wearing detection according to the segmented target area image to obtain a wearing detection result. The edge server can read a preset alarm strategy issued by the central server and determine a target alarm strategy based on the monitoring equipment identification and the wearing detection result. The edge server can generate alarm prompt information according to the target alarm strategy and send the alarm prompt information to corresponding alarm equipment. The edge server can also send the wearing detection result to the center server, so that the center server feeds back the detection result to the user terminal, the wearing detection based on the electric protection equipment is marginalized, the operation pressure of the center server is reduced, the detection time delay is reduced, and the warning prompt is timely carried out on constructors who do not wear the electric protection equipment.
In this embodiment, the server may determine a target warning policy according to the device identifier and the detection result corresponding to the monitoring device, generate warning prompt information according to the target warning policy, and send the warning prompt information to the corresponding warning device, so that the warning device can timely give a warning prompt to a constructor who does not wear the electric protection equipment according to the warning prompt information, and does not need to manually view video data or supervise the constructor to wear the electric protection equipment on the electric construction site, thereby effectively saving the labor cost and reducing the potential safety hazard in the electric construction process.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a power protective equipment-based wear detection apparatus comprising: an image acquisition module 402, a human body recognition module 404, an image segmentation module 406, a wearing detection module 408 and an alarm prompt module 410, wherein:
an image obtaining module 402, configured to obtain image data acquired by the monitoring device, where the image data includes a human body image.
And the human body recognition module 404 is configured to call a preset human body recognition model, and input a human body image into the human body recognition model to obtain a plurality of region types.
And the image segmentation module 406 is configured to segment the human body image according to the region type to obtain a corresponding target region image.
And a wearing detection module 408, configured to acquire standard equipment information corresponding to the area type, and perform wearing detection on the target area image based on the standard equipment information.
And the warning prompt module 410 is configured to generate warning prompt information when the wearing detection result indicates that the equipment is not worn.
In an embodiment, the wearing detection module 408 is further configured to obtain a monitoring device identifier corresponding to the monitoring device; determining corresponding monitoring information according to the monitoring equipment identifier; and acquiring a standard equipment list corresponding to the monitoring information, wherein the standard equipment list comprises an area type and standard equipment information corresponding to the area type.
In an embodiment, the wearing detection module 408 is further configured to perform face recognition on a face image to obtain a face feature; determining a corresponding personnel identifier according to the face characteristics, and acquiring work order information corresponding to the personnel identifier; and acquiring standard equipment information corresponding to the plurality of area types according to the work order information.
In one embodiment, the wear detection module 408 is further configured to invoke a wear detection model corresponding to the area type; inputting the target area image into a wearing detection model, and carrying out wearing detection based on standard equipment information; and acquiring a wearing detection result output by the wearing detection model.
In an embodiment, the alarm prompting module 410 is further configured to determine a target alarm policy based on a monitoring device identifier and a wearing detection result of the monitoring device; and generating alarm prompt information according to the target alarm strategy, and sending the alarm prompt information to corresponding alarm equipment.
For specific limitations of the wearing detection device based on the electric protection equipment, reference may be made to the above limitations of the wearing detection method based on the electric protection equipment, and details are not repeated here. The modules in the wearing detection device based on the electric protection equipment can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing wearing detection data based on the electric power protection equipment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power protective equipment based wear detection method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned embodiments of the power protection equipment-based wear detection method when executing the computer program.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps in the above-described power protective equipment-based wear detection method embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A power protective equipment-based wear detection method, the method comprising:
acquiring image data acquired by monitoring equipment, wherein the image data comprises a human body image;
calling a preset human body recognition model, and inputting the human body image into the human body recognition model to obtain a plurality of region types;
segmenting the human body image according to the region type to obtain a corresponding target region image;
acquiring standard equipment information corresponding to the area type, and carrying out wearing detection on the target area image based on the standard equipment information;
and when the wearing detection result indicates that the equipment is not worn, generating alarm prompt information.
2. The method of claim 1, wherein the obtaining standard equipment information corresponding to the area type comprises:
acquiring a monitoring device identifier corresponding to the monitoring device;
determining corresponding monitoring information according to the monitoring equipment identifier;
and acquiring a standard equipment list corresponding to the monitoring information, wherein the standard equipment list comprises the area type and standard equipment information corresponding to the area type.
3. The method of claim 1, wherein the target area image comprises a face image, and the obtaining of standard equipment information corresponding to the area type comprises:
carrying out face recognition on the face image to obtain face features;
determining a corresponding personnel identifier according to the human face characteristics, and acquiring work order information corresponding to the personnel identifier;
and acquiring standard equipment information corresponding to the area types according to the work order information.
4. The method of claim 1, wherein the wear detection of the target area image based on the standard equipment information comprises:
calling a wearing detection model corresponding to the region type;
inputting the target area image into the wearing detection model, and carrying out wearing detection based on the standard equipment information;
and acquiring a wearing detection result output by the wearing detection model.
5. The method according to any one of claims 1 to 4, wherein when the wearing detection result is that the equipment is not worn, the generating of the alarm prompt message comprises:
determining a target alarm strategy based on the monitoring equipment identification and the wearing detection result of the monitoring equipment;
and generating the alarm prompt information according to the target alarm strategy, and sending the alarm prompt information to corresponding alarm equipment.
6. A wear detection device based on power protective equipment, the device comprising:
the monitoring device comprises an image acquisition module, a monitoring module and a monitoring module, wherein the image acquisition module is used for acquiring image data acquired by the monitoring device, and the image data comprises a human body image;
the human body recognition module is used for calling a preset human body recognition model, inputting the human body image into the human body recognition model and obtaining a plurality of region types;
the image segmentation module is used for segmenting the human body image according to the region type to obtain a corresponding target region image;
the wearing detection module is used for acquiring standard equipment information corresponding to the area type and carrying out wearing detection on the target area image based on the standard equipment information;
and the warning prompt module is used for generating warning prompt information when the wearing detection result indicates that the equipment is not worn.
7. The apparatus according to claim 6, wherein the wearing detection module is further configured to obtain a monitoring device identifier corresponding to the monitoring device; determining corresponding monitoring information according to the monitoring equipment identifier; and acquiring a standard equipment list corresponding to the monitoring information, wherein the standard equipment list comprises the area type and standard equipment information corresponding to the area type.
8. The device of claim 6, wherein the wearing detection module is further configured to perform face recognition on the face image to obtain a face feature; determining a corresponding personnel identifier according to the human face characteristics, and acquiring work order information corresponding to the personnel identifier; and acquiring standard equipment information corresponding to the area types according to the work order information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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| CN201911380690.7A CN111199200A (en) | 2019-12-27 | 2019-12-27 | Wearing detection method and device based on electric protection equipment and computer equipment |
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| CN201911380690.7A CN111199200A (en) | 2019-12-27 | 2019-12-27 | Wearing detection method and device based on electric protection equipment and computer equipment |
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Application publication date: 20200526 |