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A vision-driven framework for robotic garment stitching: edge-aware perception and trajectory planning.

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Τίτλος: A vision-driven framework for robotic garment stitching: edge-aware perception and trajectory planning.
Συγγραφείς: Bao, Nengsheng1 (AUTHOR), Wang, Kewei1 (AUTHOR), Simeone, Alessandro2 (AUTHOR) alessandro.simeone@polito.it, Fan, Yuchen2 (AUTHOR), Xiang, Qingjiang1 (AUTHOR), Liang, Yali1 (AUTHOR), Bao, Runxuan3 (AUTHOR)
Πηγή: Journal of Intelligent Manufacturing. Jul2026, p1-23. 23p.
Θεματικοί όροι: Edge detection (Image processing), Robotic path planning, Attention, Manipulators (Machinery), Seams (Sewing), Visual perception
Περίληψη: The garment sewing industry faces persistent operator shortages and inconsistent seam quality. This work presents a vision-based robotic system for automating the perimeter-stitch, i.e. the Yun operation in shirt manufacturing, covering cuff, collar, and pocket-flap assembly. An improved Holistically-nested Edge Detection (HED) network is proposed, incorporating a directionally-aware Frequency-Spatial (FS) attention module that preserves spatial coordinate information and enhances boundary localisation in texture-rich garment images. A domain-specific dataset was constructed using garment-panel images captured under front-lit and backlit illumination conditions. The improved model achieves an Optimal Dataset Scale (ODS) F-measure of 0.8517, improving over the baseline HED model by 4.02 percentage points in ODS and 4.21 percentage points in Optimal Image Scale (OIS). Comparisons with representative edge-detection models, including PiDiNet, DexiNed, and EDTER, further demonstrate that the proposed model achieves a favourable balance between accuracy and efficiency. Stitch coordinates are generated in approximately 10 s via contour extraction and polygon approximation. The MS6MT six-degree-of-freedom (6-DOF) manipulator executes Cartesian-space linear and circular-arc trajectories derived from these coordinates through camera and eye-to-hand calibration. Physical experiments demonstrate stitch placement accuracy within ± 1 mm, validating the feasibility of the system under controlled experimental conditions. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A vision-driven framework for robotic garment stitching: edge-aware perception and trajectory planning.
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– Name: Abstract
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  Data: The garment sewing industry faces persistent operator shortages and inconsistent seam quality. This work presents a vision-based robotic system for automating the perimeter-stitch, i.e. the Yun operation in shirt manufacturing, covering cuff, collar, and pocket-flap assembly. An improved Holistically-nested Edge Detection (HED) network is proposed, incorporating a directionally-aware Frequency-Spatial (FS) attention module that preserves spatial coordinate information and enhances boundary localisation in texture-rich garment images. A domain-specific dataset was constructed using garment-panel images captured under front-lit and backlit illumination conditions. The improved model achieves an Optimal Dataset Scale (ODS) F-measure of 0.8517, improving over the baseline HED model by 4.02 percentage points in ODS and 4.21 percentage points in Optimal Image Scale (OIS). Comparisons with representative edge-detection models, including PiDiNet, DexiNed, and EDTER, further demonstrate that the proposed model achieves a favourable balance between accuracy and efficiency. Stitch coordinates are generated in approximately 10 s via contour extraction and polygon approximation. The MS6MT six-degree-of-freedom (6-DOF) manipulator executes Cartesian-space linear and circular-arc trajectories derived from these coordinates through camera and eye-to-hand calibration. Physical experiments demonstrate stitch placement accuracy within ± 1 mm, validating the feasibility of the system under controlled experimental conditions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10845-026-02941-0
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        Text: English
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              Text: Jul2026
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