Yongzhi Min1,2,*, Jiafeng Li3, Yaxing Li1
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 941-962, 2023, DOI:10.32604/cmc.2023.041182
Abstract To guarantee the safety of railway operations, the swift detection of rail surface defects becomes imperative. Traditional methods of manual inspection and conventional nondestructive testing prove inefficient, especially when scaling to extensive railway networks. Moreover, the unpredictable and intricate nature of defect edge shapes further complicates detection efforts. Addressing these challenges, this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network (UPerNet) tailored for rail surface defect detection. Notably, the Swin Transformer Tiny version (Swin-T) network, underpinned by the Transformer architecture, is employed for adept feature extraction. This approach capitalizes on the global information present in the image… More >
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