No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-Scale Interaction

Li, Gongyang; Han, Chengjun; Li, Zhi

doi:10.1109/tim.2023.3293561

IEEE Trans. Instrum. Meas.

2023

DOI: 10.1109/tim.2023.3293561

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No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-Scale Interaction

Gongyang Li

Chengjun Han

Zhi Li

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Cited by 2 publications

References 41 publications

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SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

Liu,

Cao,

et al. 2023

IEEE Access

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Automatic 2D vision-based defect detection on sealing nail (SealN) surfaces is challenging due to interference of complex backgrounds with non-homogeneous and low contrast between foreground and background. Inspired by an interesting observation that the albedo domain recovered by the uncalibrated photometric stereo (UPS) shows obvious differences and significant abruptness between defects' and nondefects' regions, we develop a novel semantic-guided variational model (SGVM) to conditional extract structural defects from albedo map. Specifically, SGVM utilizes one developed global regularized label indicator to semantically guide one local regularized relative Gaussian filter (RGF) for achieving large-scale structures (i.e., defects) preservation and small-scale textures (i.e., background) suppression. Furthermore, defects can be efficiently extracted by thresholding the structure map within the label indicator. Additionally, experimental results on numerous challenging defect images reveal that the proposed SGVM outperforms the existing advanced 2D methods in terms of defect extraction. INDEX TERMSSemantic-guided Variational Model (SGVM), Defect Extraction, Sealing Nail, Albedo Domain, Uncalibrated Photometric Stereo (UPS).

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SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

Liu,

Cao,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Region and Edge-Aware Network for Rail Surface Defect Segmentation

Qiu,

Liu,

Liu

et al. 2024

IEEE Trans. Instrum. Meas.

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No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-Scale Interaction

Cited by 2 publications

References 41 publications

SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

SGVM: Semantic-Guided Variational Model for Sealing Nail Defect Extraction Within Albedo Domain via Photometric Stereo

Region and Edge-Aware Network for Rail Surface Defect Segmentation

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