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| Safety detection system of rail transportation equipment for transmission lines based on image recognition |
| WANG Haiyan1， HOU Kang2,3,4 |
| 1. Huizhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Huizhou, Guangdong 516003, China; 2. School of Mathematical Sciences, Soochow University, Suzhou, Jiangsu 215031, China; 3. Kunshan Industrial Technology Research Institute Co., Ltd., Suzhou, Jiangsu 215316, China; 4. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China |
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Abstract To improve the operational safety of rail transportation equipment for mountainous transmission lines in forest areas, the safety detection system of rail transportation equipment based on image recognition was established. The electronic control system of rail transportation equipment for mountainous transmission lines in the forest areas was provided, and the various sensors used in the perception module system were introduced. The Faster-RCNN algorithm based on the combination of split-attention networks and self-calibration convolutions was applied to obtain better feature extraction, and the improved Faster-RCNN algorithm was used for the surrounding personnel recognition experiments. The remote control software of rail transportation equipment for transmission lines was developed based on QT, and the remote control of the equipment was realized. The results show that the improved Faster-RCNN algorithm can significantly improve the accuracy of identifying personnel around equipment in strong lighting and complex environments of forest areas. The mean average precision of image recognition can reach 87.13%, which is 74.35% higher than conventional Faster-RCNN and 76.28% higher than cascaded Faster-RCNN. The results fully prove that the improved Faster-RCNN algorithm has excellent recognition ability and ensures the safe operation of railway transportation equipment for mountainous transmission lines in forest areas.
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Received: 21 September 2023
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2024, Vol. 45 
