Research on Intelligent Detection of State of Catenary Puller Bolt Based on Deep Learning

被引：0

作者：

Cheng, Duncheng ^{[1
]}

Wang, Qian ^{[1
]}

Wu, Fuqing ^{[1
]}

Wang, Xinyu ^{[1
]}

Niu, Yingjie ^{[1
]}

Ye, Zhuang ^{[1
]}

机构：

[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu,611756, China

来源：

Tiedao Xuebao/Journal of the China Railway Society | 2021年 / 43卷 / 11期

关键词：

Bolts - Deep learning - Object recognition - Overhead lines - Railroad plant and structures - Railroad transportation - Semantic Segmentation - Semantics;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The puller bolts of the support device of high-speed railway catenary may be loosened or fall off during the long-term operation of the train. In order to address the difficulty in determining these problems due to insufficient defect samples and the small granularity of state changes in the picture, two deep learning algorithms named SSD512 and U-net8 were proposed. Based on the SSD512 positioning algorithm and the design of the U-net8 semantic segmentation model, the intelligent detection of puller bolt state was realized. Firstly, the target detection algorithm called SSD512 was used to locate the puller bolt area. Then, the semantic segmentation algorithm called U-net8 was used to mark the semantic information such as thin nuts and screws in the puller bolt pictures in different colors. Through the judgment of semantic picture, the state detection of puller bolt was realized. Training and testing were performed on the two datasets, namely the locating of the puller bolts and the semantic segmentation of the puller bolts. The experimental results show that the method proposed can achieve a comprehensive accuracy of 95.75% in the intelligent state detection of catenary puller bolts. © 2021, Department of Journal of the China Railway Society. All right reserved.

引用

页码：52 / 60