MRSDI-CNN: Multi-Model Rail Surface Defect Inspection System Based on Convolutional Neural Networks

Author:

Zhang Hui,Song Yanan,Chen Yurong,Zhong Hang,Liu Li,Wang Yaonan,Akilan Thangarajah,Wu Q. M. Jonathan

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Changsha Science and Technology Project

Special Funds for the Construction of Innovative Provinces in Hunan Province

Postdoctoral Innovative Talent Support Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 52 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Uncertainty inspired domain adaptation network for rail surface defect segmentation;Engineering Applications of Artificial Intelligence;2024-09

2. Detection and Classification of Railway Track Surface Erosion-caused Holes and Scratches Defects Based on YOLOv5s;Tehnicki vjesnik - Technical Gazette;2024-08-15

3. Improving Pavement Crack Segmentation Using Attention Mechanism and Self-gated Activation;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

4. LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces;Engineering Applications of Artificial Intelligence;2024-07

5. Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect Inspection;IEEE Transactions on Intelligent Transportation Systems;2024-07

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