AUTHOR=Ding Jian , Cao Haonan , Ding Xulin , An Chenghui 

TITLE=High Accuracy Real-Time Insulator String Defect Detection Method Based on Improved YOLOv5

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.928164

DOI=10.3389/fenrg.2022.928164

ISSN=2296-598X

ABSTRACT=Insulator string is a special insulation component, which plays an important role in overhead transmission lines. However, working outdoors for a long time, insulators often have defects because of various environment and weather conditions, which affect the normal operation of transmission lines and even cause huge economic losses. Therefore, insulator defect recognition is a crucial issue. Traditional insulator defect identification relies on manual work, which is time consuming and inefficient. Therefore, the use of artificial intelligence to detect the defect location and recognize the class of it has become a key research field. By improving the classical YOLOv5 (you only look once) model, this paper proposes a new method to enable high accuracy and real-time detection. Our method has three advantages: (1) Efficient-IoU (EIoU) replaces intersection over union (IoU) to calculate the loss of box regression, which overcomes that the detection is sensitive to various scales insulators in aerial images. (2) Since YOLOv5 itself detect some natural scene in real word, some anchors setting by default are not suitable for defect detection, this paper introduces Assumption-free K-MC2 (AFK-MC2) algorithm into YOLOv5 to modify K-means algorithm to improve accuracy and speed. (3) The cluster non-maximum suppression (Cluster-NMS) algorithm is introduced to avoid missing detection of insulators because of mutual occlusion in images and improve the computation speed at the same time. The experiments results show that this model can improve detection accuracy compared with YOLOv5 and realize real time detection.