Artificial Intelligence‐Based Defect Prediction in Photovoltaic Panels Coupling with Nondestructive Detection Techniques: A Comprehensive Review

Defects in photovoltaic (PV) panels significantly influence solar PV generation, necessitating accurate determination of defect location, size, and severity. Conventional detection methods suffer from limitations such as complexity, long cycle times, and high costs, which hinder rapid, large‐scale reliability assessments. Recently, imaging approaches have emerged as nondestructive, rapid, and high‐throughput alternatives for PV defect evaluation. This review provides a systematic investigation of the research progress at the intersection of imaging‐based methods and AI algorithms for PV defect detection. The advantages and disadvantages of integrating imaging techniques with AI algorithms across various aspects of PV panel defect evaluation are comprehensively discussed to clarify the application prospects for practical defect assurance in PV panels. Compared to traditional inspection methods, the integrated approach combining imaging‐based techniques with AI algorithms enables real‐time, precise, and intelligent defect detection in PV panels. Furthermore, future trends of combining these imaging techniques with AI algorithms across various aspects of PV panel defect evaluation are also comprehensively introduced in this study.