Research on Small Sample Data-Driven Inspection Technology of UAV for Transmission Line Insulator Defect Detection

Abstract

Insulators are important safety devices on high-voltage transmission lines. An insulator inspection system based on UAVs is widely used. Insulator defect detection is performed against two main engineering problems: 1. The scarcity of defect images, which leads to a network overfitting problem. 2. The small object detection, which is caused by the long aerial photography distance, and the low resolution of the insulator defect area pictures. In this study, firstly, the super-resolution reconstruction method is used to augment the dataset, which can not only solve the overfitting problem but also enrich the image texture features and pixel values of defect areas. Secondly, in the process of insulator defect detection, a two-stage cascading method is used. In the first stage, the rotated object detection algorithm is used to realize the object location of insulator strings, and then images of the identified insulators are cropped to reduce the proportion of the background area in defect images. In the second stage, YOLO v5 is used for the detection of insulator caps that are missing defects. The method proposed shows good detection effect on the self-built training set which contains only 85 images captured from real inspection environments. The method has practical industrial application value.

Transmission line defect detection based on feature fusion

“…Chen et al used the improved Faster-RCNN algorithm to detect bolt defects [4] , but the detection efficiency is slow and the FPS is only 22, which can't satisfy the function of real-time detection. Pan et al expanded the dataset based on the superresolution reconstruction method [5] , which solved the problem of less defective data in industrial scenarios, but caused the problem of model overfitting. Bao et al achieved the detection of damping hammers by Cascade RCNN [6] , but the number of model parameters was large and difficult to deploy.…”

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

“…Numerous scholars domestically and internationally have conducted extensive research on the issue of insulator faults. The research is primarily categorized into classical feature extraction and artificial intelligence methods (Antwi-Bekoe et al, 2022;Pan et al, 2022). Classical feature extraction techniques include binarization, binary support vector machines (Zhou et al, 2020), edge detection algorithms based on Canny (Cao et al, 2020), and multiscale feature extraction (Zhang et al, 2020;Siu et al, 2022).…”

MI-YOLO: more information based YOLO for insulator defect detection

“…3 Unfortunately, traditional manual inspection has become obsolete due to its low efficiency and accuracy, 4 high cost and risks, 5 and heavy reliance on personal experience. 6 Other reasons for the decline of manual inspection include the rise of unmanned aerial vehicles 7 and advancements in artificial intelligence. 8 Automatic patrol detection approaches based on airborne crafts and computer-aided techniques can be categorized into three groups: image processing-based (IP) methods, 9 machine learning-based (ML) methods, 10 and deep learning-based (DL) methods.…”

“…Unfortunately, traditional manual inspection has become obsolete due to its low efficiency and accuracy, 4 high cost and risks, 5 and heavy reliance on personal experience 6 . Other reasons for the decline of manual inspection include the rise of unmanned aerial vehicles 7 and advancements in artificial intelligence 8 …”