AI Visual Inspection for Garment Production

2026-08-16

AI Summary

研究开发并验证了基于CNN的AI视觉检测系统，用于服装生产线的质量控制，解决了手动检测不一致的问题，但模型准确性受训练数据多样性影响。

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.

Problem

• garment manufacturing
• quality control
• sewing defects
• manual inspection
• defect detection

Innovation

• Convolutional Neural Networks
• visual inspection system
• garment quality control
• fabric color diversity
• generalization ability