Computer Vision-Based Anomaly Diagnosis in Knitted Fabrics: A Graph-Theoretic Approach to Stitch Defect Localization

Jing Jin

Article
2026 / Volume 9 / Pages 197-225
Received 2 July 2025; Accepted 16 September 2025; Published 28 January 2026
https://doi.org/10.31881/TLR.2026.197

Abstract
Automated quality inspection remains a critical challenge in textile manufacturing, where subtle fabric defects can significantly impact product value and performance. Existing computer vision systems struggle to simultaneously achieve precise stitch-level defect localization and robust generalization across diverse knitting patterns and deformations. This paper presents a novel hybrid architecture combining dual-branch convolutional neural networks with edge-conditioned graph neural networks, where visual features inform topological graph representations of stitch connectivity patterns for comprehensive anomaly detection. Experimental results demonstrate superior performance with 91.3% detection accuracy (vs. 85.7% for commercial systems), 92.8% overall recall, with strong performance on critical defect categories, and real-time processing at 20 FPS, while reducing the overall false positives rate to 2.3%, and up to 8.9% improvement over the baseline 3D CNN on connectivity defects. The proposed framework establishes a new paradigm for fabric inspection by integrating computer vision with structural graph analysis, achieving both high precision and practical deployment efficiency for industrial quality control applications.

Keywords
computer vision, knitted fabric defect detection, edge-conditioned convolution, textile quality control, industrial inspection