Author(s)

Tao Zhou, Qian Huang, Xiaolong Zhang, Yong Zhang

State Grid Yangquan Power Supply Company, Yangquan, China.

The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.

Substation, Target Detection, Deep Learning, Multi-Layer Feature Fusion, Unmanned Vehicles

Zhou, T. , Huang, Q. , Zhang, X. and Zhang, Y. (2023) Intelligent Detection Method of Substation Environmental Targets Based on MD-Yolov7. Journal of Intelligent Learning Systems and Applications, 15, 76-88. doi: 10.4236/jilsa.2023.153006.