Improved YOLOv7 transmission line warning ball and bolt visual recognition

Installation of warning balls on transmission lines can prevent pedestrians and vehicles from approaching the wires and reduce safety hazards, while prolonged operation and the impact of weather can cause loose damage to the warning ball, so it is necessary to dismantle and replace the warning ball in time. In order to meet the demand for efficient installation of warning balls by robots instead of human beings, this paper researches the accurate recognition of warning balls and bolts, and proposes the improved YOLOv7 target detection algorithm to recognize warning balls and bolts. The improved YOLOv7 algorithm is formed by embedding the ECA channel attention module in the backbone module, adopting the feature fusion network of WBFPN, and choosing a more advanced loss function EIOU Loss. The experimental results show that the mAP value of the improved YOLOv7 algorithm reaches 94.7% and the FPS reaches 57.76f/s, which proves that the improved algorithm can satisfy the recognition requirements during the installation process.