An Industrial Carpet Defect Dataset and a Hybrid Two-Stage Inspection Framework for Automated Quality Control

Automated visual inspection has become a critical component of Industry 4.0 manufacturing systems, enabling improved product quality, reduced operational costs, and increased production efficiency. In the carpet manufacturing industry, quality inspection remains particularly challenging due to the high variability of textures, colors, patterns, and defect appearances, which often limits the effectiveness of conventional inspection systems. Furthermore, the scarcity of publicly available industrial carpet defect datasets limits the realistic assessment of automated inspection methods. Existing approaches are therefore frequently evaluated on benchmark datasets that do not fully capture the variability of real manufacturing environments or rely on computationally demanding end-to-end deep learning architectures, which may reduce their suitability for practical industrial deployment. To address these challenges, this paper introduces a new industrial carpet defect dataset acquired under controlled imaging conditions and containing defect-free samples together with four representative defect categories: Backing Seam, Bad Mend, Dirty Face, and End Out. In addition, we present an engineering study that systematically evaluates a practical two-stage inspection framework combining pretrained deep feature extraction, one-class anomaly detection, and supervised defect classification for industrial carpet quality control. A distinguishing aspect of this study is the systematic evaluation of the framework on both the public MVTec AD Carpet benchmark and the newly introduced industrial dataset, enabling its performance to be assessed under both benchmark and realistic industrial conditions. The first stage performs anomaly detection using one-class learning models trained exclusively on defect-free samples, while the second stage classifies detected defects into predefined categories. To investigate the impact of visual representations on inspection performance, multiple deep feature extraction backbones, including ConvNeXt, VGG-19, and Wide ResNet-50-2, are evaluated in combination with lightweight machine learning models. Experimental results demonstrate strong inspection performance, achieving an AUROC of 99.92% on the MVTec AD Carpet benchmark and 93.92% on the proposed industrial carpet dataset. The obtained results demonstrate that this integration of established techniques provides an effective and computationally lightweight solution for industrial deployment, while confirming the practical value of the proposed dataset as a realistic benchmark for future research.