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.
To address these limitations, this research proposes the integration of Large Language Models (LLMs) with CNN-based visual inspection technology to enhance learning capability, adaptability, and defect recognition across a wider range of garment materials. Building upon the intelligent manufacturing principles established by Professor Ray Wai Man Kong, the proposed approach demonstrates significant potential to reduce reliance on manual inspection, improve quality consistency, decrease production waste, and accelerate the adoption of smart automation in garment manufacturing.
Keywords: AI Visual Inspection, Garment Manufacturing, Computer Vision, Deep Learning, Quality Assurance, Automation.
Title: AI Visual Inspection for Garment Production
Author: Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 3, July 2026 - September 2026
Page No: 47-64
Research Publish Journals
Website: www.researchpublish.com
Published Date: 15-August-2026
