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This study aims to develop an AI-driven fabric inspection system that utilizes simulation images for effective defect detection.

FabricDefectNet: An AI-Driven Fabric Inspection System Utilizing Simulation Images as Reference Information

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This study aims to develop an AI-driven fabric inspection system that utilizes simulation images for effective defect detection.
Developed FabricDefectNet for automating textile inspection.
Employed LightGlue matcher for feature matching between simulation and real fabric images.
Utilized Thin Plate Spline warping to correct non-rigid deformations before detecting anomalies.
Achieved accurate detection of color mismatches and pattern errors without using actual defects.
Demonstrated effective pixel-wise anomaly detection using cosine distances between fabric patches.

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Randomized trial demonstrates enhanced defect detection in fabrics, suggesting improvements in textile quality control.

Liu et al. (2026) studied this question.

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