服装生产的AI视觉检测
AI Visual Inspection for Garment Production
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中文总结 AI 辅助
针对服装生产中人工缝纫线检测存在的缺陷漏检等问题，开发基于CNN的AI视觉检测系统，在多种颜色面料上测试发现其对部分颜色面料的跳针缺陷检测有效，模型精度受训练数据多样性影响。
中文摘要 AI 辅助
服装制造业正面临着提升产品质量、降低成本以及加速向工业4.0数字化转型的日益增长的压力。最具挑战性的质量控制活动之一是缝纫线检测，其中断线、跳针等缺陷很难通过人工检测持续稳定地发现。人工检测常受疲劳、主观判断和不稳定表现的影响，导致缺陷漏检、返工和生产效率降低。本研究开发并验证了一种基于人工智能（AI）的服装缝纫线质量控制视觉检测系统，该系统利用卷积神经网络（CNNs）检测缝纫缺陷，初始训练采用黑色面料和黑色缝纫线样本。实验测试在黑色、红色、深绿色、浅蓝色、银色和荧光黄色面料上进行，结果显示该系统能成功检测黑色、红色和深绿色材料上的跳针缺陷，但在检测断线缺陷以及浅蓝色、银色和荧光黄色等视觉特性差异显著的面料时，性能存在局限。这些发现表明，模型精度受训练数据多样性以及跨不同面料和缝纫线颜色的泛化能力的强烈影响。
英文摘要
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.
