LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production

Abstract

Reliable fabric inspection is difficult to automate because production-line imagery differs substantially from the controlled samples used by many public benchmarks. This paper introduces LUSITANOv2, a high-resolution fabric-defect dataset collected at an active textile inspection station with an industrial line-scan camera and directional illumination. It contains 25,120 native images, including both defect-containing and defect-free fabric, together with 18,557 class-agnostic bounding boxes. We establish supervised baselines with YOLOv12n, Faster R-CNN, and RT-DETR-L and evaluate nine one-class anomaly-detection methods. RT-DETR-L achieves the highest mAP@0.5 in the matched detector comparison, reaching 0.610, compared with 0.597 for Faster R-CNN and 0.592 for YOLOv12n. In a separate controlled YOLOv12n resolution study, retaining more native weave detail improves detection performance, reaching 0.598 mAP@0.5 at the highest tested resolution. A post hoc analysis finds no simple recall penalty for defects that touch the image boundary. Matched zero-shot experiments across LUSITANOv2, TILDA, and ZJU-Leaper reveal substantial cross-dataset degradation despite stronger in-domain results. LUSITANOv2 provides a realistic benchmark for localized defect detection, one-class anomaly detection, resolution-sensitive line-scan processing, and future work on transfer to active textile-production settings.