Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 2 2026 04 01 Automated Surface Defect Detection in Copper Blanks Using YOLOv8 Segmentation and EfficientNetV2-S Classification 169 181 3726 10.22044/jadm.2026.16853.2818 EN Hossein Ghayoumi Zadeh Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran. Ali Fayazi Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Khosro Rezaee Department of Biomedical Engineering, Meybod University, Meybod, Iran Afsaneh Aminaee Director of Training and Competency Development, Sarcheshmeh Copper Complex, Rafsanjan, Iran. Hadi Halavati Research and Development Division, Sarcheshmeh Copper Complex, Rafsanjan, Iran. Mehdi Tahernejad Technical and Engineering Research, Research and Development Department, Sarcheshmeh Copper Complex, Rafsanjan, Iran. Hadi Memarzadeh Head of Operations, Refinery and Casting Division, Sarcheshmeh Copper Complex, Rafsanjan, Iran. Ali Masoumi Expert, Refinery and Casting Division, Sarcheshmeh Copper Complex, Rafsanjan, Iran Mohammad Sadegh Jafari Technical and Engineering Research, Research and Development Department, Sarcheshmeh Copper Complex, Rafsanjan, Iran. Journal Article 2025 09 20 In this study, an intelligent deep learning–based system is proposed for automated detection of surface defects in copper cathode blanks used in the electrorefining process. The proposed pipeline combines a YOLOv8-based segmentation model with an EfficientNetV2-S classifier to localize and analyze defect-relevant regions of each blank. The segmentation module identifies the main copper regions, edge strips, and defect-prone areas associated with surface anomalies such as scratches, dents, misalignment, and discoloration, effectively reducing background interference and improving classification reliability. The dataset includes 5,266 labeled images with a significant class imbalance, addressed using focal loss and class weighting during training. Experimental results on the test set demonstrate strong performance, achieving 98.32% accuracy, 96.71% precision, 95.67% recall, an F1-score of 96.19%, and an AUC of 0.9953. Grad-CAM visualizations and error analysis further confirm that the model consistently focuses on meaningful defect regions while remaining robust to background and illumination variations. These results highlight the effectiveness of the proposed approach for reliable quality control in industrial copper electrorefining lines. https://jad.shahroodut.ac.ir/article_3726_468cf35851bd44a50cd25dff85635b74.pdf