Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm-Reference-Cited by-同舟云学术

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Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm

Published:2024-10 Issue:5 Volume:38 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Wang Mengyi¹,Zhou Yu²^ORCID

Affiliation:

1. Postgraduate Student, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Key Laboratory of Structural Durability and System Safety of Shanghai Rail Transit, Tongji Univ., 4800 Cao’an Rd., Shanghai 201804, PR China.

2. Professor, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Key Laboratory of Structural Durability and System Safety of Shanghai Rail Transit, Tongji Univ., 4800 Cao’an Rd., Shanghai 201804, PR China (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4816

Reference34 articles.

1. Numerical modeling of rolling contact fatigue cracks in the railhead

2. Microstructural investigation on a rail fracture failure associated with squat defects

3. Research on the Propagation Characteristics of Fatigue Cracks on Rail Surfaces

4. Res2Net: A New Multi-Scale Backbone Architecture

5. Girshick R. J. Donahue T. Darrell and J. Malik. 2014. “Rich feature hierarchies for accurate object detection and semantic segmentation.” In Proc. 2014 IEEE Conf. On Computer vision and Pattern Recognition 580. New York: IEEE.

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