A Novel Method of Object Detection Based on YOLOV8 and PixelAttention in Transmission Lines

Abstract

In recent years, a large amount of debris has appeared on power transmission lines, affecting circuit power supply and endangering human safety. At present, deep learning based object detection algorithms have made continuous progress, but they still cannot meet the practical application requirements of real-time performance. It is necessary to further reduce model complexity and improve detection speed. Therefore, a transmission line debris recognition based on fusion point attention improved YOLOV8 is proposed. The model uses the original YOLOV8 model as the base model and adds PixelAttention in detection to enhance the model's receptive field and multi-scale object perception, thereby improving the model detection accuracy. The experimental results show that the average accuracy of the original model is only 92.3%, while the average accuracy of the improved model has increased to 94%.