Mrs. P.G.V. Rekha, B.Priyadarshini, B.Lavanya, N.Sai ManiKumar, K.Manmohan Singh 03 03 The detection of surface defects in solar cells is crucial for ensuring the efficiency and reliability of photovoltaic systems This paper proposes an automated solar cell surface defect detection system based on an optimized YOLOv5 deep learning model The system detects and localizes multiple defect types including microcracks scratches contamination broken grids and discoloration in real time Optimization strategies including custom anchor tuning mosaic augmentation and transfer learning maximize detection performance Experimental results demonstrate mAP05 of 943 precision of 931 recall of 918 with realtime inference at 48 FPS Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026