A novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder for small negative samples

Abstract

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre‐trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto‐Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine‐tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self‐explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self‐explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

Electrical insulator defect detection with incomplete annotations and imbalanced samples

“…This demonstrates that the Pi-Index achieved competitive results on the annotated insulators in IDID. Furthermore, the IDID dataset lacks annotations at positions ( 6), ( 7), (10), (11), and (12). Based on the aforementioned analysis, we further compared the performance of YOLO v5, Faster RCNN, and Pi-Index on unannotated insulators.…”

“…For the relatively small insulators, all methods correctly classified the insulators at positions (1), (3), and (10). The positions that are easily undetected or misclassified by these methods are ( 6), (7), and (11). According to the IDID's annotations, M-YOLO v3 missed the insulators at positions ( 4), (6), and (7).…”

“…D-YOLO v4 missed 3 insulators at positions ( 5), (7), and (8). YOLO v5 missed the insulator at position (9) and had false alarms at positions ( 8) and (11), in which the "Good" insulators were identified as "Good" and "FlashDamaged". Both Pi-FT and Pi-Index detected the "Good" insulator at position (11) as both "Good" and "FlashDamaged".…”

“…The N a and N b indicate the number of anchors and predicted boxes, respectively. For an arbitrary anchor a j , we first match it with the predicted boxes and then determine the matched box using Equation (11).…”

“…It is incapable of providing quick feedback on the condition of insulators and meeting the inspection needs of a modern smart grid, namely more frequent inspection, repair, and maintenance [8]. Due to the low cost, miniaturization, and high mobility of unmanned aerial vehicle (UAV) inspection, it has replaced manual inspection and become the mainstream inspection scheme for power equipment [9][10][11][12]. For insulator defect detection, the UAV inspection collects a large number of aerial images with insulators.…”