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