Infrared Image Recognition of Substation Equipment Based on Yolov5-GDE

With the development of infrared detection technology and the increased demand for intelligent identification of substation equipment, infrared substation equipment detection networks with lightweight parameters and high detection accuracy have been the focus of research. To meet this demand, we propose an infrared object detection network called YOLOv5-GDE. To ensure the speed of model detection, we choose You Only Look Once v5s (YOLOv5s) as the basic framework and design a backbone network of diverse branches for enhancing the feature extraction capability of the network and deploying it efficiently without increasing the number of parameters and model complexity. In addition, we introduce efficient multi-scale attention to increase the receptive field using multi-scale parallel branching, and enhance the global and local feature interactions through cross-space information aggregation methods to improve the stability and accuracy of target detection. Moreover, we redesigned the ordinary convolutional module in the model and the C3 module of the neck network to reduce the number and complexity of model parameters. Furthermore, we improve the loss function so that the network converges quickly during the training process. The experimental results show that the proposed YOLOv5-GDE network reaches 95.8% mean Average Precision (mAP0.5) in the infrared substation equipment dataset. Compared with the original model, the number of parameters decreases by 36.9%. The proposed architecture is able to meet the accuracy and real-time requirements of substation equipment identification, and provides conditions for subsequent fault diagnosis of substation equipment.

I. Introduction

Since the 21st century, due to the rapid improvement of science and technology in China, China has become the country with the highest voltage level and the largest power grid in the world. As a key link in grid operations, the functioning of its equipment has a direct impact on the power supply of the grid. Since the substation operates outdoors, it is affected by seasons, weather and foreign matter invasion for a long time, and the equipment is prone to rust, insulation aging and other problems, which in turn affects the safety and stability of the power grid system. Therefore, timely understanding of the operational status of substation equipment and improving the efficiency and accuracy of fault hidden danger detection is the current key work of electric power personnel.