Detection of railway catenary insulator defects based on improved YOLOv5s

Jing Tang; Minghui Yu; Minghu Wu

doi:10.7717/peerj-cs.1474

Detection of railway catenary insulator defects based on improved YOLOv5s

PeerJ Comput Sci. 2023 Jul 14:9:e1474. doi: 10.7717/peerj-cs.1474. eCollection 2023.

Authors

Jing Tang^{1

2}, Minghui Yu², Minghu Wu^{1

2}

Affiliations

¹ Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China.
² School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China.

Abstract

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.

Keywords: Defect detection; Triplet attention; Railway catenary insulator; YOLOv5s.

Grants and funding

This work was supported by the Special Project of Central Government for Local Science and Technology Development of Hubei Province No. 2019ZYYD020, the Natural Science Foundation of Hubei Province No. 2022CFA007, and the Hubei University of Technology Ph. D. Research Startup Fund Project No. BSQD2020014. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.