Tang Jing, Yu Minghui, Wu Minghu
Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China.
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China.
PeerJ Comput Sci. 2023 Jul 14;9:e1474. doi: 10.7717/peerj-cs.1474. eCollection 2023.
In this article, a method of railway catenary insulator defects detection is proposed, named RCID-YOLOv5s. In order to improve the network's ability to detect defects in railway catenary insulators, a small object detection layer is introduced into the network model. Moreover, the Triplet Attention (TA) module is introduced into the network model, which pays more attention to the information on the defective parts of the railway catenary insulator. Furthermore, the pruning operations are performed on the network model to reduce the computational complexity. Finally, by comparing with the original YOLOv5s model, experiment results show that the average precision (AP) of the proposed RCID-YOLOv5s is highest at 98.0%, which can be used to detect defects in railway catenary insulators accurately.
本文提出了一种铁路接触网绝缘子缺陷检测方法，名为RCID-YOLOv5s。为了提高网络检测铁路接触网绝缘子缺陷的能力，在网络模型中引入了小目标检测层。此外，在网络模型中引入了三重注意力（TA）模块，该模块更加关注铁路接触网绝缘子缺陷部位的信息。此外，对网络模型进行剪枝操作以降低计算复杂度。最后，通过与原始YOLOv5s模型比较，实验结果表明，所提出的RCID-YOLOv5s的平均精度（AP）最高，为98.0%，可用于准确检测铁路接触网绝缘子的缺陷。
