Steel surface defect detection is essential for maintaining industrial production quality. However, traditional single-stage detectors often face a trade-off between localization and classification, limiting their ability to distinguish visually similar or irregular defects. This study proposes a novel explainable hybrid AI CAD framework that separates these tasks into two stages. The detection stage utilizes Fusion YOLO, which integrates an adopted DCBS-YOLO with YOLOv9c and YOLOv8s to perform class-agnostic binary detection, thereby optimizing defect localization. The classification stage employs a hybrid model combining ensemble CNNs and Vision Transformer (ViT) to capture both local textures and global dependencies. The entire pipeline is optimized via an MLOps-based auto hyperparameter tuning, with Grad-CAM providing explainability. Experiments on the NEU-DET dataset show that Fusion YOLO achieves an AP of 83.8%, and the classification stage reaches a 99.7% F1-score. Furthermore, the framework's generalization capability was validated on the GC10-DET dataset, achieving a 71.5% mAP (detection) and a 94.8% F1-score (classification), confirming its robustness for reliable industrial inspection.
Moon et al. (Sat,) studied this question.
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