Automatic Detection Method for Small Size Transmission Lines Defect Based on Improved YOLOv3

Abstract

Defect detection methods based on machine learning extremely accelerate the transmission lines routine inspection process. In this paper, we propose an automatic defect detection method based on improved YOLOv3. Random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction. Focal loss function, which focus on differentiating between easy and hard examples, is employed to deal with the class imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.