CN113887310A - Worker standard dressing identification method, system and medium - Google Patents
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Abstract
The embodiment of the invention discloses a worker standard dressing identification method, a worker standard dressing identification system and a worker standard dressing identification medium, wherein the method comprises the following steps: acquiring real-time image or video data of a monitored area and corresponding position information; detecting and classifying the image or video data based on a worker target detection model and an image classification model, identifying workers who don't dress normally on the image, and sending out an alarm according to corresponding position information of the workers; and outputting the standard dressing identification result of the worker.
Description
Technical Field
The invention relates to the field of electric power personnel safety, in particular to a worker standard dressing identification method, a worker standard dressing identification system and a worker standard dressing identification medium.
Background
In order to ensure the safety of the personnel working with electricity, the personnel working at each working place must wear the safety helmet, wear safety shoes, etc. according to the regulations. However, some substations are far away, and workers can wear safety helmets without being dressed according to regulations due to low safety awareness, but the workers cannot be effectively supervised due to lack of operation and maintenance personnel, so that potential safety hazard risks exist.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a worker standard dressing identification method which can realize automatic identification of worker standard dressing in a transformer substation.
The invention also provides a worker standard dressing identification system using the worker standard dressing identification method.
The invention also provides a computer readable storage medium for implementing the worker specification dressing identification method.
According to the worker specification dressing identification method in the embodiment of the first aspect of the invention, the method comprises the following steps: acquiring real-time image or video data of a monitored area and corresponding position information; detecting and classifying the image or video data based on a worker target detection model and an image classification model, identifying workers who don't dress normally on the image, and sending out an alarm according to corresponding position information of the workers; and outputting a standard dressing identification result of a worker.
The worker standard dressing identification method provided by the embodiment of the invention at least has the following beneficial effects: according to the invention, real-time image/video data of the monitoring area is acquired, detection and classification of worker standard dressing are automatically carried out through the worker target detection model and the image classification model, and warning reminding is sent according to the worker standard dressing identification result, so that the potential risk of safety accidents is reduced.
According to some embodiments of the invention, the method further comprises: training the worker target detection model and the image classification model through worker image data of a monitoring area, and specifically comprising the following steps: acquiring worker image data, and constructing worker image annotation data meeting requirements according to worker target detection and worker standard dressing classification annotation requirements to obtain a target detection data set and an image classification data set; performing data enhancement on the target detection data set and the image classification data set to obtain an enhanced target detection data set and an enhanced image classification data set; constructing a worker target detection model based on the deformation DETR, and constructing an image classification model based on a mobilenetv3 classification network; training a worker target detection model by using the enhanced target detection data set to obtain a trained worker target detection model; and training the image classification model by using the enhanced image classification data set to obtain a trained image classification model.
According to some embodiments of the invention, the data enhancing the object detection data set and the image classification data set comprises at least one of: rotation, inversion, color transformation, brightness transformation, and contrast transformation.
According to some embodiments of the invention, the detecting and classifying the image or video data based on the worker target detection model and the image classification model comprises: inputting an image or a video to be detected, and performing worker detection on the image or the video to be detected based on the worker target detection model to obtain each worker image of the image to be detected or each worker screenshot in the video to be detected; and performing classification prediction on whether the clothing is standardized or not on each worker image or each worker screenshot based on the image classification model.
According to some embodiments of the invention, said alerting according to its corresponding location information comprises: and broadcasting the warning information which is not in standard dressing through a loudspeaker corresponding to the monitoring area according to the position information.
According to some embodiments of the invention, the outputting the worker specification dressing recognition result comprises: and integrating the worker standard dressing identification results of each monitoring area, and visualizing the worker standard dressing identification results.
According to the worker specification dressing identification system of the second aspect embodiment of the present invention, the worker specification dressing identification method of any one of the first aspect embodiments of the present invention is used, and the worker specification dressing identification method includes: the system comprises an image/video acquisition module, a signal transmission module and a cloud server module; the image/video acquisition module is arranged in the transformer substation and used for acquiring image or video data of a monitored area; the signal transmission module is in communication connection with one or more image/video acquisition modules and is used for receiving a first video signal transmitted by the image/video acquisition modules, processing the first video signal to obtain a second video signal and transmitting the second video signal to the cloud server module; the cloud server module is provided with an algorithm module so as to realize classification prediction of standard dressing of workers.
The worker specification dressing identification system provided by the embodiment of the invention at least has the following beneficial effects: according to the invention, the video data of the transformer substation are acquired through the image/video acquisition module and transmitted to the cloud server module through the signal transmission module, so that the recognition of standard dressing of workers is realized, and the prompt of standard dressing is sent through the loudspeaker, so that the potential safety hazard risk is reduced.
According to some embodiments of the invention, the image/video capture module comprises: a high-definition camera; the holder carries the high-definition camera and is used for realizing the stable rotation of the high-definition camera; and the loudspeaker is connected with the signal transmission module and used for sending out a reminding sound according to the control instruction of the signal transmission module.
According to some embodiments of the present invention, the signal transmission module includes a compression coding unit, configured to perform compression coding on the first video signals collected from the plurality of image/video collection modules to obtain the second video signal.
According to some embodiments of the invention, the cloud server module comprises a memory for storing the second video signal and a parallel computing device for processing the second video signal to obtain a classification prediction result of worker canonical dresses.
According to some embodiments of the invention, the algorithm module comprises an object detector and an image classification model.
According to some embodiments of the invention, the target detector is a deformation DETR target detector.
According to some embodiments of the invention, the image classification model is a mobilenetv3 image classification model.
According to some embodiments of the invention, the holder is adapted to rotate at regular intervals by a certain angle.
The computer-readable storage medium according to an embodiment of the third aspect of the invention has stored thereon a computer program which, when executed by a processor, performs the method of any of the embodiments of the first aspect of the invention.
All the benefits of the first aspect of the present invention are obtained in that the computer-readable storage medium of an embodiment of the present invention has stored thereon computer-executable instructions for performing the worker-specific dress identification method according to any one of the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a model training method according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of the model for performing the standard dressing detection classification on the worker according to the embodiment of the present invention.
FIG. 4 is a block diagram of the modules of the system of an embodiment of the present invention.
FIG. 5 is a schematic diagram of a training detection process of the system of the embodiment of the present invention applied to a worker's normative dress.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and more than, less than, more than, etc. are understood as excluding the present number, and more than, less than, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Referring to fig. 1, the worker specification dressing identification method of the embodiment of the present invention includes the following steps: acquiring real-time image or video data of a monitored area and corresponding position information; detecting and classifying the image or video data based on a worker target detection model and an image classification model, identifying workers who don't dress normally on the image, and sending out an alarm according to corresponding position information of the workers; and outputting a standard dressing identification result of a worker.
Referring to fig. 2, in some embodiments, methods of embodiments of the present invention further comprise: training the worker target detection model and the image classification model through worker image data of a monitoring area, and specifically comprising the following steps: acquiring worker image data, and constructing worker image annotation data meeting requirements according to worker target detection and worker standard dressing classification annotation requirements to obtain a target detection data set and an image classification data set; performing data enhancement on the target detection data set and the image classification data set to obtain an enhanced target detection data set and an enhanced image classification data set; constructing a worker target detection model based on the deformation DETR, and constructing an image classification model based on a mobilenetv3 classification network; training a worker target detection model by using the enhanced target detection data set to obtain a trained worker target detection model; and training the image classification model by using the enhanced image classification data set to obtain the trained image classification model.
In some embodiments, data enhancing the target detection data set and the image classification data set includes at least one of: rotation, inversion, color transformation, brightness transformation, and contrast transformation.
Referring to fig. 3, in some embodiments, the detection classification of the image or video data based on the worker target detection model and the image classification model includes: inputting an image or a video to be detected, and performing worker detection on the image or the video to be detected based on a worker target detection model to obtain each worker image of the image to be detected or each worker screenshot in the video to be detected; and performing classification prediction on whether the clothing is standardized or not on each worker image or each worker screenshot based on the image classification model.
In some embodiments, issuing an alert based on its corresponding location information includes: and broadcasting the warning information which is not in standard dressing through a loudspeaker corresponding to the monitoring area according to the position information.
In some embodiments, outputting the worker specification dressing recognition result comprises: and integrating the standard dressing identification results of workers in each monitoring area, and visualizing the standard dressing identification results of the workers.
Referring to fig. 4, an embodiment of the present invention further provides a worker specification dressing identification system, and the worker specification dressing identification method is used in the system, where the system includes an image/video acquisition module, a signal transmission module, and a cloud server module.
The image/video acquisition module is arranged in the transformer substation and used for acquiring image or video data; the image/video acquisition module specifically comprises a high-definition camera, a holder and a loudspeaker; the holder carries a high-definition camera and is used for realizing the stable rotation of the high-definition camera; and the loudspeaker is connected with the signal transmission module and used for sending out a reminding sound according to the control instruction of the signal transmission module. In the embodiment, the high resolution of the high-definition camera is beneficial to standardizing the dressing state identification; the cloud platform utilizes single camera to realize the control in wider range through steady rotation. Image signals collected by the high-definition camera can be transmitted to the video transmission module through the optical fiber. The loudspeaker is responsible for sending out and reminds the sound, can play the warning standard dress order that sends from signal transmission module to in time remind the workman of unnormal dress.
The signal transmission module is in communication connection with one or more image/video acquisition modules, and comprises a compression coding unit which is used for carrying out compression coding on high-definition video signals acquired by the image/video acquisition modules and transmitting the video signals to the cloud server module through a wired network. The signal transmission module is also responsible for receiving worker irregular dressing detection results sent back by the cloud server module and broadcasting reminding by using a loudspeaker of the image/video acquisition module.
The cloud server module is configured with an algorithm module to achieve classification prediction of worker standard dressing. The cloud server module comprises a memory and a parallel computing device, wherein the memory can provide a cloud storage function and is used for storing the compressed and coded video signal, and the video signal can be stored in a hard disk. The memory may also store a plurality of model results for the algorithm module, as well as training data, logs, and the like. The parallel computing device can provide a cloud computing function and is used for processing the video signals to obtain a classification prediction result of worker standard clothing. The cloud computing service is composed of elastic distribution computing services, and appropriate parallel computing devices are distributed according to computing requirements of the algorithm modules. The algorithm module includes an object detector and an image classification model.
The algorithm module realizes the standard dressing identification of workers through a worker detection algorithm and a standard dressing classification algorithm. The staff detection algorithm uses a visual transform-based target detection algorithm: morph DETR (Deformable DETR). Compared with a convolutional neural network, the transformer utilizes a self-attention module to realize more flexible feature extraction capability, and can effectively deal with the detection of workers at various angles and under various working states and shielding conditions. The deformation DETR introduces the idea of deformation convolution on the basis of DETR, overcomes the defects of long training time and poor detection effect on small targets based on the original DETR, and improves the performance of target detection. The canonical clothing recognition algorithm is mobilenetv3, which is a classification network based on a convolutional neural network. The worker's normative dressing includes whether to wear safety helmets, whether to wear work clothes, whether to wear work shoes, whether to wear gloves, etc. In order to improve the classification performance, the target detection result is utilized to capture the image from the original image, and compared with the target detection, the image is down-sampled to a certain degree, so that the images used in the image classification do not need to be down-sampled, the resolution ratio is higher, and the classification performance is favorably improved.
Referring to fig. 5, the flow of the embodiment of the present invention during application is as follows: the cameras of the video acquisition modules monitor the environment under each scene, and the cloud deck carrying the cameras rotates by a certain angle at regular intervals to realize the maximum monitoring area. The camera monitoring video is transmitted to the video transmission module of each transformer substation through the optical fiber, the video transmission module encodes multiple paths of videos, compressed video signals are transmitted to the cloud server through the wired network and stored in the hard disk of the cloud server. And performing worker detection on the stored images shot by the cameras by using a deformation DETR target detection algorithm by using parallel computing equipment of the cloud server, and storing all worker screenshots in the video. Whether the worker screenshot is subjected to the classification prediction of standard dressing or not is carried out by utilizing a mobilenetv3 classification algorithm, the result is transmitted to a video transmission module of the transformer substation through a wired network, the result is broadcasted at the transformer substation through a loudspeaker, the worker corresponding to a monitoring area is prompted to carry out the non-standard dressing behavior, and the danger is avoided.
Although specific embodiments have been described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are equally within the scope of this disclosure. For example, any of the functions and/or processing capabilities described in connection with a particular device or component may be performed by any other device or component. In addition, while various illustrative implementations and architectures have been described in accordance with embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications of the illustrative implementations and architectures described herein are also within the scope of the present disclosure.
It should be recognized that the method steps in embodiments of the present invention may be embodied or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method may use standard programming techniques. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented by hardware or combinations thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more microprocessors. The computer program includes a plurality of instructions executable by one or more microprocessors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A worker standard dressing identification method is characterized by comprising the following steps:
acquiring real-time image or video data of a monitored area and corresponding position information;
detecting and classifying the image or video data based on a worker target detection model and an image classification model, identifying workers who don't dress normally on the image, and sending out an alarm according to corresponding position information of the workers;
and outputting a standard dressing identification result of a worker.
2. The worker canonical wear identification method of claim 1, wherein the method further comprises: training the worker target detection model and the image classification model through worker image data of a monitoring area, and specifically comprising the following steps:
acquiring worker image data, and constructing worker image annotation data meeting requirements according to worker target detection and worker standard dressing classification annotation requirements to obtain a target detection data set and an image classification data set;
performing data enhancement on the target detection data set and the image classification data set to obtain an enhanced target detection data set and an enhanced image classification data set;
constructing a worker target detection model based on the deformation DETR, and constructing an image classification model based on a mobilenetv3 classification network;
training a worker target detection model by using the enhanced target detection data set to obtain a trained worker target detection model;
and training the image classification model by using the enhanced image classification data set to obtain a trained image classification model.
3. The worker specification rigging identification method according to claim 2, wherein the data enhancement of the target detection data set and the image classification data set includes at least one of: rotation, inversion, color transformation, brightness transformation, and contrast transformation.
4. The worker canonical wear identification method according to claim 1, wherein the detection classification of the image or video data based on a worker target detection model and an image classification model comprises:
inputting an image or a video to be detected, and performing worker detection on the image or the video to be detected based on the worker target detection model to obtain each worker image of the image to be detected or each worker screenshot in the video to be detected;
and performing classification prediction on whether the clothing is standardized or not on each worker image or each worker screenshot based on the image classification model.
5. The worker specification dressing identification method according to claim 1, wherein said issuing an alert according to its corresponding location information comprises: and broadcasting the warning information which is not in standard dressing through a loudspeaker corresponding to the monitoring area according to the position information.
6. The worker canonical wear identification method according to claim 1, wherein the outputting of the worker canonical wear identification result includes: and integrating the worker standard dressing identification results of each monitoring area, and visualizing the worker standard dressing identification results.
7. A worker specification dressing identification system using the worker specification dressing identification method according to any one of claims 1 to 6, comprising:
the system comprises an image/video acquisition module, a signal transmission module and a cloud server module;
the image/video acquisition module is arranged in the transformer substation and used for acquiring image or video data of a monitored area;
the signal transmission module is in communication connection with one or more image/video acquisition modules and is used for receiving a first video signal transmitted by the image/video acquisition modules, processing the first video signal to obtain a second video signal and transmitting the second video signal to the cloud server module;
the cloud server module is provided with an algorithm module so as to realize classification prediction of standard dressing of workers.
8. The worker specification dressing identification system of claim 7, wherein the image/video capture module comprises:
a high-definition camera;
the holder carries the high-definition camera and is used for realizing the stable rotation of the high-definition camera;
and the loudspeaker is connected with the signal transmission module and used for sending out a reminding sound according to the control instruction of the signal transmission module.
9. The worker-specification dressing identification system according to claim 7, wherein the signal transmission module includes a compression coding unit for compression coding the first video signals collected from the plurality of image/video collection modules to obtain the second video signal.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 6.
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| CN116977919A (en) * | 2023-06-21 | 2023-10-31 | 北京卓视智通科技有限责任公司 | Method and system for identifying dressing specification, storage medium and electronic equipment |
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| CN111401314A (en) * | 2020-04-10 | 2020-07-10 | 上海东普信息科技有限公司 | Dressing information detection method, device, equipment and storage medium |
| CN111832465A (en) * | 2020-07-08 | 2020-10-27 | 星宏集群有限公司 | Real-time head classification detection method based on MobileNet V3 |
| CN111898514A (en) * | 2020-07-24 | 2020-11-06 | 燕山大学 | Multi-target visual supervision method based on target detection and action recognition |
| CN113255719A (en) * | 2021-04-01 | 2021-08-13 | 北京迈格威科技有限公司 | Target detection method, target detection device, electronic equipment and computer-readable storage medium |
| CN113297913A (en) * | 2021-04-26 | 2021-08-24 | 云南电网有限责任公司信息中心 | Method for identifying dressing specification of distribution network field operating personnel |
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| CN114445748A (en) * | 2022-01-28 | 2022-05-06 | 深圳市中云慧通科技有限公司 | Video human body feature detection and linkage alarm method and storage medium |
| CN116977919A (en) * | 2023-06-21 | 2023-10-31 | 北京卓视智通科技有限责任公司 | Method and system for identifying dressing specification, storage medium and electronic equipment |
| CN116977919B (en) * | 2023-06-21 | 2024-01-26 | 北京卓视智通科技有限责任公司 | Method and system for identifying dressing specification, storage medium and electronic equipment |
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