地毯制造质检的痛点——人工目检慢、主观、不稳定——在AI时代有了系统解法，做工业视觉质检的团队可以直接参考这套从数据采集到模型迭代的完整路径。
本文提出了一种用于地毯制造在线质检的机器视觉系统设计方案，核心目标是实时检测缺陷并系统收集标注数据，以持续训练质检AI模型。方案基于六西格玛DMAIC项目背景，针对织机增加后的产能瓶颈和缺陷率问题，设计了线扫描相机与组合照明子系统，并定义了地毯专用缺陷分类体系。建模策略从无监督异常检测起步，通过人工标注闭环逐步演进到监督检测与分割模型。该方案将数据采集作为一等工程目标，而非事后补充，为工业质检AI落地提供了可部署的完整蓝图。
Data Collection for Training Quality-Control AI in Carpet Manufacturing
Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at the line speeds and widths of modern looms. We present a design proposal for an in-line machine-vision system whose primary purpose is twofold: to inspect the carpet web in real time and, equally importantly, to systematically collect and label images of defect patterns so that increasingly capable quality-control models can be trained over the life of the installation.The proposal is grounded in a concrete industrial setting: a Six Sigma (DMAIC) project at a woven-carpet production facility that anticipated a production bottleneck following the installation of additional weaving machines, with a substantial baseline defect rate and significant financial exposure associated with quality failures. We describe an imaging subsystem based on synchronized line-scan cameras with combined bright-field and grazing illumination, derive the resolution and throughput requirements needed to resolve fine structural defects across a multi-metre web, and define a carpet-specific defect taxonomy.We then lay out a staged modelling strategy that begins with unsupervised anomaly detection trained on defect-free material, following the paradigm exemplified by the carpet category of the MVTec Anomaly Detection benchmark, and matures through a human-in-the-loop annotation flywheel into supervised detection and segmentation models. Finally, we connect detection performance to the DMAIC objectives, showing how reductions in escaped defects translate into improved process quality and process sigma levels. The contribution is an end-to-end, deployable blueprint that treats data collection as a first-class engineering objective rather than an afterthought.