A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and h...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 11; p. 5249
Main Authors:	Zhang, Tong, Zhang, Yinan, Xin, Min, Liao, Jiashe, Xie, Qingfeng
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-06-2023 MDPI
Subjects:	Accuracy Algorithms Boxes convolutional block attention module Defects Drone aircraft Eigenvalues ghost module insulator and defect detection keyword light-weight Localization Methods Neural networks Power lines Semantics Sensors Unmanned aerial vehicles Weight reduction YOLOv5 insulator and defect detection convolutional block attention module YOLOv5 unmanned aerial vehicles keyword light-weight ghost module
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Summary:	Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s23115249