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

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 environmental 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 its class has become a key research field. By improving the classical YOLOv5 (you only look once) model, this article 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 scale insulators in aerial images. 2) Since YOLOv5 itself detects some natural scenes in the real world, some anchors setting by default are not suitable for defect detection, this article introduces Assumption-free K-MC2 (AFK-MC2) algorithm into YOLOv5 to modify the 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.

A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices

“…The YOLOv5 target detection algorithm was released by Ultralytics in 2020 and has high accuracy and fast inference, making it one of the best performing target detection models available today [35]. The YOLOv5 model can be separated into four parts: Input, Backbone, Neck, and Detect.…”

Online insulator defects detection and application based on YOLOv7-tiny algorithm

“…Proposed the Faster R-Transformer, specifically tailored for efficient insulator detection in complex aerial environments, showcasing adaptability to diverse operational scenarios. Furthermore, (Ding et al, 2022), presented a high-accuracy real-time insulator string defect detection method based on Improved YOLOv5, highlighting precision and timely identification of potential issues. However, these methods have primarily been tested through server-side deployment, and the exploration of online detection using edge computing devices on unmanned UAV remains relatively unexplored.…”

A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

“…Currently, this algorithm is among the most widely used in industry. This literature [18] proposes several methods to solve the problem of insulator masking, which can cause missed detections. The proposed approaches include introducing the EIoU concept in calculating regression loss, using the AFK-MC2 algorithm for training, and applying a clustering NMS algorithm for eliminating redundant bounding box predictions.…”