Coarse-to-fine-grained insulator defect detection using two-stage deep learning framework

Introduction In power transmission and power distribution systems, insulators are the fundamental components which provide mechanical support and electrical isolation between conductors and supporting towers. However, these insulators are continuously exposed to extreme weather conditions, mechanical stress, pollution and dust, which can lead to surface damage and abnormal operation. Method Conventionally, manual on-site inspection is performed to identify faulty insulators. However, this method is labor intensive, risky, and reactive. With the advancement in unmanned aerial vehicle (UAV) technology and deep learning method, automatic visual inspection has replaced the conventional methods. However, automatic visual inspection from UAV images is challenging task due to complex background and small size of the insulators. To address these challenges, we proposed a framework that consists of two stages. In the first stage, you only look once (YOLO)-based is used for extracting candidate region of insulator to get coarse class prediction. During the second stage, the candidate regions are then cropped from the image into image patches and fed to a lightweight patch-level classifier. In order to enhance inter-class separability, hard-pair contrastive classification loss is proposed by adding hard-pair contrastive term to the cross-entropy loss in the embedding space. Discussion The proposed technique is evaluated using an open-source insulator defect image dataset. The experiment results demonstrate the superior performance of the proposed framework compared with the YOLO variants and representative two-stage detectors.