Improved YOLOV7 Algorithm for Transmission Line Metal Fittings Equipment Detection: Algorithm Improvement for Integrating Attention Mechanism

Abstract

Metal fittings equipment is widely used in the power grid system. In order to improve the speed and accuracy of personnel's inspection of transmission lines, and to widely detect arc burns of various metal fittings equipment, this paper designs a metal fittings equipment detection algorithm based on improved YOLOV7. This method adds a CA attention mechanism to the network structure of YOLOV7 to enhance the feature extraction of hardware devices in the network model. At the same time, it reduces the interference of complex backgrounds on the network model to extract features of hardware devices, allowing the network model to extract features in detail, thereby improving the network model's detection generalization for hardware devices. The experimental results showed that the improved YOLOV7 method improved the Precision of detecting metal fittings equipment from 78.6% to 81.3%, and the Recall in the test set increased from 77.3% to 79.6%.