Affiliation:
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.
Funder
Special Project of Central Government
Natural Science Foundation of Hubei Province
Hubei University of Technology Ph. D. Research Startup Fund Project
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