Safety production is of great significance to the development of enterprises and society. Accidents often cause great losses because of the particularity environment of electric power. Therefore, it is important to improve the safety supervision and protection in the electric power environment. In this paper, we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real time. In this method, the human body key points in video frames were first extracted by the high-resolution network, and then classified in real time by spatial-temporal graph convolutional network. Experimental results show that this method can effectivel
detect illegal actions in the simulated scene.
YANG Jian1 (杨坚), LI Congmin2 (李聪敏), HONG Daojian1 (洪道鉴), LU Dongqi1 (卢东祁), LIN Qiujia3 (林秋佳), FANG Xingqi2∗ (方兴其), YU Qian1 (喻谦), ZHANG Qian1 (张乾)
. Real-Time Safety Behavior Detection Technology of Indoors Power Personnel Based on Human Key Points[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(2)
: 309
-315
.
DOI: 10.1007/s12204-022-2526-2
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