From Visual Defect Detection to Maintenance Decision-Making: A Review of Computer Vision-Based Fault Diagnosis for Photovoltaic Modules

Photovoltaic (PV) modules are increasingly deployed in large-scale renewable energy systems, making reliable defect detection and fault diagnosis essential for operational safety, energy yield, and maintenance efficiency. This review systematically examines computer vision-based approaches for PV module inspection from a decision-oriented perspective. First, it summarizes representative PV defects, including cracks, hot spots, soiling, shading, delamination, corrosion, broken cells, finger interruptions, burn marks, and bypass diode-related anomalies, and discusses their visual manifestations, physical origins, diagnostic implications, and maintenance relevance. Second, it reviews major imaging modalities, including electroluminescence imaging, infrared thermography, RGB imaging, and UAV-based inspection, highlighting their complementary roles in detecting internal, thermal, and surface-level defects. Third, it analyzes key computer vision tasks, such as image classification, object detection, semantic and instance segmentation, anomaly detection, and severity assessment. The review emphasizes that PV inspection should move beyond normal/defective recognition toward quantitative and trustworthy fault diagnosis. Finally, it discusses current challenges and future perspectives, including field robustness, data scarcity, small-defect detection, multimodal fusion, explainability, uncertainty estimation, edge deployment, and human-in-the-loop maintenance decision-making. This review provides a structured reference for developing intelligent and actionable PV inspection systems.