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Open access Sep 2026

A Defect Detection Method for Rail Ultrasonic B-Scan Images Based on a Multi-Scale Dense Attention Network

Experimental results show that MSDA-Net achieved an accuracy of 0.909, with a macro Precision of 0.867, macro Recall of 0.845, and macro F1-score of 0.848, demonstrating the effectiveness of the proposed network structure for rail defect classification.

Guan-Lin Zhang, Ping Wang, Fan Guo et al. · 0 citations
Open access Sep 2026

FH-Net: Frequency-domain and hierarchical dual enhancement for few-shot surface defect segmentation

A novel few-shot surface defect segmentation method termed FH-Net is proposed, which aims to achieve high-precision segmentation of previously unseen categories using only a limited number of annotated samples and enriches support set feature diversity through foreground- and global-level amplitude perturbations in the...

Li-Li Fan, Zhang Yang, Fan Guo et al. · 0 citations

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