Research on Anti-breakage Target Detection Method of Transmission Line based on Improved YOLOv5

Abstract

Abstract Illegal construction operations and sudden outbursts in transmission corridor protection areas pose a threat to the safe and stable operation of transmission lines. It is very important to identify the transmission line breakage risk to guarantee power grid security. The existing target detection model has many parameters and a large capacity, which makes it difficult to implement the deployment of edge terminals and cannot detect the risk of transmission line outbreaks in real time. This paper proposed a transmission line anti-breach detection method based on improved YOLOv5. Firstly, the CSPdarknet53 module is replaced with a lightweight MobilenetV3 module to reduce the model parameter number. The SPP module is replaced by SimSPPF module to realize the fast conversion of multi-scale image convolution features. Finally, the ECA attention mechanism is introduced to improve the ability of the model to focus on key features, thereby improving the overall performance of the model. Experiment results show that the proposed method has a smaller parameter number and a mAP value of 97.77%, which is the best overall performance, and provides support for the realization of outbreak risk warning of transmission lines based on edge intelligence.