Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Abstract

Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object recognition accuracy image detection. The method first reduces the computation and memory consumption by introducing the RepConv structure, further improves the detection accuracy and speed of the model by embedding the C2F structure. This method finally is further optimized neural network by the Meta-ACON activation function. The results indicate that the average detection accuracy of the improved YOLOv5 network can reach 96.9%, which is 2.2% higher than before. Additionally, corresponding detection speed can reach 258.36 frames/second, which surpasses existing mainstream target detection models, performing better in terms of the balance of inference speed and detection accuracy. Consequently, the effectiveness and superiority of the algorithm have been proved.