The textile industry’s quality control has traditionally relied on manual visual inspection, a labor-intensive process prone to human error. This process constitutes a typical “vigilance task,” where sustained attention over long periods often leads to a decline in detection performance, known as the vigilance decrement, due to high cognitive load. To address these human-centric challenges, this paper proposes a Human-in-the-Loop (HITL) automated fabric inspection system designed not to replace, but to augment the capabilities of skilled inspectors. The system was implemented as a “brownfield” retrofit, upgrading an existing inspection machine with a low-cost, non-invasive hardware and software package. At its core, a Real-Time Models for object Detection (RTMDet) model is utilized to perform the primary, high-load vigilance task of defect scanning. This allows the human operator to focus on the higher-value task of verifying and classifying potential defects identified by the AI. A case study conducted in a real-world jeans manufacturing factory demonstrated that this HITL approach enhanced inspection task efficiency by approximately 2.5 times compared to traditional manual inspection, significantly reducing operator cognitive load and enabling parallel tasking. This study provides a practical blueprint for SMEs in the textile industry to implement effective, human-centric AI solutions within existing operational constraints.
Liu et al. (Thu,) studied this question.