Abstract

Fabric defect detection remains difficult when defects appear as subtle structural disruptions rather than strong color changes. This paper presents CAP-YOLO, a lightweight prior-coded input framework for YOLO-based fabric defect detection. Instead of redesigning the detector architecture, the method converts image-derived priors into RGB-compatible inputs so that multiple variants can be compared under the same YOLO26n detector, parameter count, training schedule, and data split. Two image-only priors are studied: a Delta E color-deviation prior and an anomaly-derived texture residual prior. Five variants are evaluated on the Tianchi fabric defect dataset: an RGB baseline (M0), Delta E prior coding (M1), anomaly prior coding (M2), dual-prior coding (M3), and a lightweight prior-adapter preprocessing route (M4). On the held-out test split, M2 achieves the best overall result with 0.3289 mAP50 and 0.1542 mAP50-95, improving over the RGB baseline by 18.48% in mAP50, 27.26% in precision, 9.38% in recall, and 16.62% in F1 score, while keeping the same detector size of 2.3787M parameters. Per-class analysis shows that the gains are concentrated in texture-disruptive defect categories, whereas categories dominated by diffuse appearance or subtle color spread benefit less. These results support a practical conclusion: for lightweight fabric defect detection, a simple image-derived texture residual prior is more effective than a color-only prior or naive multi-prior mixing under the current controlled setting.

Authors

Maizhe Jiang¹, Jihao Liu²
Wenzhou University, China¹, Jinggangshan University, China²

Keywords

Fabric Defect Detection, YOLO, Texture Residual Prior, Prior-Coded RGB Input, Industrial Image Processing