Surface defect detection on printed playing cards using an improved YOLOv8n model

This is the authors' abstract. We don't add key points for this paper.

Reliable online inspection of playing card surfaces is difficult because small, low-contrast defects are easily confused with dense printed graphics. This study proposes a defect-aware YOLOv8n framework integrating a ResNet50 backbone, DeCA, an improved BiFPN with SPD-Conv, a dynamic adaptive decoupled head, and Wise-IoU v3. These components strengthen weak-feature extraction, suppress background interference, preserve multiscale details, and improve localization. A production dataset of 2750 original images covering edge defect, ink missing, fold, and stain was augmented to 6000 images and divided 7:1:2 at the source-image level to prevent information leakage. Across three independent training runs, the proposed model achieved 96.27± 0.16% mAP@50 and 77.07± 0.18% mAP@50:95, corresponding to mean improvements of 12.77 and 11.64 percentage points over YOLOv8n, respectively. It also reached 97 FPS with 21.85 M parameters on the experimental GPU, indicating a favorable detector-level accuracy-speed balance under the evaluated hardware conditions. The evaluation is restricted to one production-line dataset; no external or cross-domain validation was conducted, and no generalization or transferability claim is made.