AI Visual Inspection for Garment Production

Researchers developed an AI-based visual inspection system using Convolutional Neural Networks (CNNs) to detect sewing-line defects in garment production, achieving successful detection of jump sewing-line defects on black, red, and dark green fabrics but showing limitations on light blue, silver, and fluorescent yellow fabrics. The study, published on arXiv (2608.21426v1), highlights that model accuracy depends on training data diversity and generalization across colors.

arXiv:2608.21426v1 Announce Type: new Abstract: The garment manufacturing industry is under increasing pressure to improve product quality, reduce costs, and accelerate digital transformation toward Industry 4.0. One of the most challenging quality-control activities is sewing-line inspection, where defects such as broken stitches and skipped stitches are difficult to detect consistently through manual inspection. Human-based inspection is often affected by fatigue, subjective judgement, and inconsistent performance, resulting in defect leakage, rework, and reduced production efficiency. This study presents the development and validation of an Artificial Intelligence AI -based visual inspection system for garment sewing-line quality control. The system utilizes Convolutional Neural Networks CNNs to detect sewing defects and was initially trained using black fabric and black sewing thread samples. Experimental testing was conducted on black, red, dark green, light blue, silver, and fluorescent yellow fabrics. The results demonstrated successful detection of jump sewing-line defects on black, red, and dark green materials, while performance limitations were observed for broken sewing-line defects and fabrics with significantly different visual characteristics, including light blue, silver, and fluorescent yellow colours. These findings indicate that model accuracy is strongly influenced by the diversity of training data and the ability to generalize across different fabric and thread colours.