Improved YOLOv8-Based Foreign Object Detection Method for Transmission Lines

In the field of foreign object detection on transmission lines, advanced detection technologies are crucial. Existing methods have limitations in detecting multiple types of foreign objects, leading to a decrease in detection accuracy. To address these challenges, this paper introduces a new attention-augmented detection head method for foreign object detection, which uses the YOLOv8 model and incorporates Mixed Local Channel Attention (MLCA) and Auxiliary Detect Head (DetectAux) modules. These modules efficiently utilize image data to perform multi-object detection tasks, thereby improving detection performance under various environmental conditions. An integrated RepBlock module further enhances feature extraction. The proposed solution significantly improves the accuracy of foreign object detection in different scenarios, providing a more robust and adaptable approach to transmission line foreign object detection. The experimental results demonstrate the superiority of the proposed method in this paper for high-precision and highspeed recognition under complex background conditions.