Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model

Abstract

Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background.

Research on Safety Detection of Transmission Line Disaster Prevention Based on Improved Lightweight Convolutional Neural Network

“…The deep convolutional network Model E, in the process of extracting feature information of aerial images through continuous conventional convolution operations, loses the feature information of the target of disaster prevention and safety detection of transmission lines in the aerial images to a certain extent [51]. To comprehensively improve the information mining ability of the Model E transmission line disaster prevention and safety detection model for aerial images, and at the same time to avoid a large amount of information loss of both ends of the nodes caused by repeated use of the cycle, it was decided that the BiFPN network was to be improved in the Head layer network.…”

An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

“…To verify the practicability of the improved YOLOv3 model proposed in this paper for insulator fault detection, experiments were conducted on four network models: YOLOv3, YOLOv3-dense [38], CSPD-YOLO [39], and our proposed model (improved YOLOv3 model). The four models were trained and then tested on the same dataset "InSF-detection" for a fair comparison.…”

“…Experimental results show that the average precision of the proposed model can reach 94.47%, however, since the fault area of insulator is relatively small, the fault area will be lost after the feature extraction network of YOLOv3-dense, resulting in a decrease in the accuracy of insulator fault detection. In [39], a Cross Stage Partial Network was introduced to YOLOv3, and the Cross Stage Partial Dense YOLO (CSPD-YOLO) model was proposed to detect insulator faults. The proposed model achieves a good effect on insulator fault detection (with the average precision of 98.18%), however, the memory usage of the proposed model (265 MB) is larger than that of YOLOv3 (240 MB).…”

“…However, there are several challenges in performing insulator fault detection based on deep learning methods, including a limited amount of insulator fault images, the extreme smallness of the insulator fault area, the complexity of the background of aerial images, and so on. To solve these issues, based on our previous work [38,39] and DenseNet, an improved YOLOv3 model is proposed for insulator fault detection in diverse aerial images from high-voltage transmission lines. To solve the problem of insufficient fault images, a novel insulator fault dataset named "InSF-detection" is created; to enhance feature reuse and feature propagation of high-resolution feature layer, DenseNets are adopted to the feature extraction network of improved YOLOv3 network model; to improve the accuracy and robustness of insulator fault detection, Spatial Pyramid Pooling networks (SPP-networks) and improved multi-scale prediction network are employed to the improved YOLOv3 network model.…”

Assembly Quality Detection Based on Class-Imbalanced Semi-Supervised Learning

“…Due to its data-driven nature, the fast-developing deep learning algorithms can extract knowledge from historical data, reducing the dependence on expert domain knowledge and avoiding artificial design of visual features. They have been widely used in the detection of key components of high-speed trains [7][8][9][10], fault diagnosis [11][12][13], high-voltage transmission line detection [14][15][16], and other industrial applications.…”