Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 10 May 2023 / Approved: 11 May 2023 / Online: 11 May 2023 (05:21:17 CEST)

A peer-reviewed article of this Preprint also exists.

Zhang, T.; Zhang, Y.; Xin, M.; Liao, J.; Xie, Q. A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5. Sensors 2023, 23, 5249. Zhang, T.; Zhang, Y.; Xin, M.; Liao, J.; Xie, Q. A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5. Sensors 2023, 23, 5249.

Abstract

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art network of object detection, YOLOv5, has been widely used on insulator and defect detection. However, YOLOv5 network has some limitations like 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 Ghost module into YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance in 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 module (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) 0.5 and the mAP0.5:0.95 of our model can reach 99.4% and 91.7%, the parameters and model weight are reduced to 3807372 and 8.79M, which can easily deploy to embedded devices like UAVs. And the speed of detection can reach 10.9ms/image, which can meet the real-time detection requirement.

Keywords

keyword light-weight; insulator and defect detection; YOLOv5; Ghost module; convolutional block attention module; unmanned aerial vehicles

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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