In apparel production systems, the effectiveness of quality control directly shapes operational productivity, cost stability, and long-term brand credibility. Defects in fabric—ranging from structural inconsistencies and surface impurities to distortions in weave or print—do not remain isolated anomalies; when undetected, they advance through successive manufacturing stages, amplifying material waste and compounding financial losses. Despite its widespread use, manual inspection relies heavily on human judgment, making it vulnerable to variability, fatigue-induced error, and limited throughput capacity. Technological developments in computer vision and deep learning have introduced automated inspection models that promise consistent, high resolution detection of fabric anomalies. The trajectory of these technologies reflects a broader methodological shift: early rule-based image processing approaches have progressively given way to supervised learning algorithms and, more recently, convolutional neural network (CNN) architectures capable of hierarchical feature extraction. Although laboratory evaluations frequently demonstrate strong classification performance, translating these results into industrial environments remains complex. Practical constraints—including insufficiently diverse datasets, fluctuating illumination conditions, rapid fabric movement, processing delays, and compatibility with existing production infrastructure—continue to hinder reliable deployment. This study situates automated fabric defect detection within a broader sociotechnical context. While AI-enabled inspection systems may reduce textile waste and improve quality consistency, their implementation introduces new energy demands and infrastructural requirements that complicate sustainability assessments. Accordingly, the research reframes automation not as a discrete technological substitution, but as an organizational and systemic reconfiguration. To support implementation, a structured framework is advanced that integrates data governance, model refinement strategies, cost-benefit evaluation, and lifecycle analysis. The findings suggest that industrial adoption is determined less by peak algorithmic performance than by the system’s adaptability to manufacturing realities, its economic justification, and its compatibility with sustainable production objectives.
Automated Fabric Defect Detection in Apparel Manufacturing , Fabric defect detection, artificial intelligence in apparel manufacturing, computer vision–based fabric inspection, deep learning techniques for textile quality control
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