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Rail surface defect detection using a U-Net convolutional neural network
Other ConferencesTo address the challenges of high labor intensity and low efficiency in traditional manual inspection, the use of traditional pixel segmentation methods has issues such as algorithm incompatibility, stringent lighting condition consistency requirements ...
Qin Sun +4 more
semanticscholar +1 more source
J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl.
Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations.
Samyuktha Sasi Sekaran, M. Subaji
semanticscholar +1 more source
Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations.
Samyuktha Sasi Sekaran, M. Subaji
semanticscholar +1 more source
MRSDI-CNN: Multi-Model Rail Surface Defect Inspection System Based on Convolutional Neural Networks
IEEE Transactions on Intelligent Transportation Systems, 2022Hang Zhong, Yaonan Wang, Hui Zhang
exaly
Self-Supervised Defect Representation Learning for Label-Limited Rail Surface Defect Detection
IEEE Sensors Journal, 2023Huan Wang, Mingjian Zuo, Zhiliang Liu
exaly
Depth Repeated-Enhancement RGB Network for Rail Surface Defect Inspection
IEEE Signal Processing Letters, 2022Wujie Zhou, Weiwei Qiu
exaly
Rail surface defect detection using a transformer-based network
Journal of Industrial Information IntegrationFeng Guo, Jian Liu, Yu Qian, Quanyi Xie
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A Coarse-to-Fine Model for Rail Surface Defect Detection
IEEE Transactions on Instrumentation and Measurement, 2019Jinrui Gan, Jianzhu Wang, Qingyong Li
exaly