TCG-AI: AI Integration in Collectibles Grading for Trading Card Games and Sport Cards

The grading of trading cards and collectibles remains a labour-intensive and subjective task, traditionally dependent on expert visual inspection. This paper investigates the integration of Artificial Intelligence (AI) into automated grading systems, with a focus on balancing accuracy, scalability and interpretability. We compare conventional image-processing methods based on OpenCV with modern Convolutional Neural Network (CNN) models and propose a hybrid framework that incorporates YOLOv8 with Oriented Bounding Box (OBB) support for localised defect detection. The system operates through a six-stage pipeline encompassing centring, corner wear, edge integrity, surface quality and holistic scoring. To support reproducibility and consistency, we developed a robotic imaging platform capable of capturing standardised high-resolution images (6000 × 4000 pixels) under controlled lighting conditions, enhanced by Flat Field Correction (FFC) for illumination normalisation. Using this platform, we constructed a large-scale dataset comprising over 43,000 training samples after augmentation, covering a wide range of real-world card conditions. Experimental evaluation using localisation accuracy, region-level grading agreement and professional grading consistency metrics demonstrates that CNN-based classification approaches significantly outperform traditional OpenCV pipelines in predictive accuracy but lack spatial interpretability. In contrast, the proposed hybrid framework achieves improved defect localisation and grading consistency relative to CNN-only baselines, while providing transparent visual explanations through YOLO-OBB detections. The integration of deterministic image analysis with deep learning enables both quantitative subgrades and interpretable defect localisation, offering an explainable and scalable solution for automated collectible card grading.