Work

Research on fault identification method of railroad fastener based on center point prediction network

Year: 2024

Type: conference-paper

Abstract: Currently, image detection based on deep learning has a good application prospect for railroad fault diagnosis. However, the existing methods of applying deep learning to fastener fault detection more or less have some drawbacks, such as computational complexity, slow detection speed, low detection accuracy, poor robustness, etc. To address these issues, this paper introduces a fastener fault identification method for railroad lines based on CenterNet. By introducing the Convolutional Block Attention Module and the jump connection mechanism, and conducting comparative experiments on different networks, it is proved that the network proposed in this paper has a better speed and accuracy for fastener detection.

Source: International Conference on Information Science, Electrical and Automation Engineering (ISEAE)

DOI: 10.1117/12.3037448

Authors: zihao Chen, zhen Han

Institutions: China Railway Signal & Communication (China), CRSC Research & Design Institute Group (China)

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4402916835