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Transformer substation safety fence crossing behavior identification method, system and equipment

A security fence and recognition method technology, applied in the field of image recognition, can solve the problem of low accuracy of behavior recognition and achieve the effect of improving accuracy

Active Publication Date: 2020-11-24
ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a substation security fence climbing behavior identification method, system and equipment, which are used to solve the technical problem of low accuracy of behavior identification in the prior art when deep learning is used to identify the behavior

Method used

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  • Transformer substation safety fence crossing behavior identification method, system and equipment
  • Transformer substation safety fence crossing behavior identification method, system and equipment
  • Transformer substation safety fence crossing behavior identification method, system and equipment

Examples

Experimental program
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Effect test

Embodiment 2

[0064] Take the video of workers climbing over the fence and the video of workers walking normally. The videos contain different weather conditions and different lighting; the video of workers climbing over the fence is used as a positive sample, and the video of workers walking normally is taken as a negative sample. Based on positive samples and negative samples Sample construction video data set D1; construct video data set D1 by obtaining positive samples and negative samples, so as to train the deep learning network in the future;

[0065] It should be further explained that the specific process of constructing video dataset D1 based on positive samples and negative samples is as follows:

[0066] Mark the positive sample and the negative sample respectively. Since the positive sample is a video of a worker climbing over the fence, the positive sample is marked as crossed. Since the negative sample is a video of the worker walking normally, the negative sample is marked as...

Embodiment 3

[0097] Such as Figure 4 As shown, a substation safety fence climbing behavior recognition system includes a video data set module 201, a skeleton point image sequence collection module 202, a motion stream video sample set module 203, an optical flow video sample set module 204, and a deep learning network training module 205 And a real-time behavior recognition module 206;

[0098] The video data set module 201 is used to obtain the video of the worker climbing over the fence as a positive sample, obtain the video of the worker walking normally as a negative sample, and construct the video data set D1 based on the positive sample and the negative sample;

[0099] The skeleton point image sequence collection module 202 is used to extract the human skeleton from the video data set D1 using a pose estimation algorithm to obtain the skeleton point image sequence collection K1;

[0100] The motion stream video sample set module 203 is used to perform tensor voting on the human s...

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Abstract

The invention discloses a transformer substation safety fence crossing behavior identification method, system and equipment. A video data set D1 is constructed on the basis of a positive sample and anegative sample, a skeleton point image sequence set K1 and an optical flow video sample set D2 are obtained on the basis of the video data set D1, tensor voting is performed on the human body skeleton point image sequence set K1 to obtain a motion stream video sample set D3, the optical flow video sample set D2 and the motion flow video sample set D3 are input into a deep learning network for modeling, and finally, real-time videos are classified through the trained deep learning network to judge whether a behavior of crossing a fence exists in the real-time videos or not. According to the embodiment of the invention, the adverse effect caused by skeleton point errors is overcome by utilizing tensor voting, and the influence of optical flow and motion flow on behavior recognition is comprehensively considered to construct the deep learning network, so that the accuracy of behavior recognition is improved.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a method, system and equipment for identifying the behavior of overcoming a substation safety fence. Background technique [0002] Substation is an important node of power network system. Repairs and maintenance of power networks often require on-site work at substations. Due to the dangerous nature of the substation environment, on-site operations are usually completed by professional and technical personnel. However, due to the large similarity between the equipment and its intervals at the substation site, even highly skilled technicians may go to the wrong interval due to negligence or misjudgment. Once the wrong equipment is operated, it will not only cause serious damage to the equipment and even the system, but also may cause harm to the personal safety of the operators. In this case, it is a very important preventive measure to monitor the workers and prevent them from...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/20G06V20/41G06N3/045G06F18/2415
Inventor 杨英仪张晓晔吴昊麦晓明王朋
Owner ELECTRIC POWER RES INST OF GUANGDONG POWER GRID