Detection of insulator defects based on improved YOLOv7 model

Abstract

Inspection for the electric transmission system has great significance for powerline maintenance, in which defects of insulators are needed to be found in time to preserve the safety of the whole system. To improve the accuracy and efficiency of insulator defect detection, computer vision techniques are employed. However, since insulator defects on the insulator strings are small objects and usually works in complex environment, it is challenging to get satisfactory detection results. In order to solve this issue, we proposed an insulator defect detection method based on YOLOv7 which is one of the state-of-the-art object detection methods. By introducing coordinate attention mechanism into the backbone network and redesigning the feature pyramid network (FPN) to have bi-directional FPN like structure, we successfully adapt the original model to the insulator defect detection task. We used an open-source dataset called CPLID to train our model. Experiments demonstrate that our method achieve good performance for insulator defect detection and have better average precision comparing with other methods. Ablation study were also designed to verify the effectiveness of the improved component.