The photovoltaic industry is a key strategic initiative in achieving carbon neutrality and emission peak and receives national support as a sunrise industry. The solar cell module is the central part of a solar power generation system, and its production quality and cost have a direct impact on the overall quality and cost of the system. The EL quality inspection is crucial for ensuring the quality of PV modules. However, traditional methods of EL quality inspection, such as manual inspection or machine vision inspection, are found to be inefficient, prone to false detections, and expensive in terms of labour costs. Additionally, these methods may lead to secondary damage to PV modules due to human intervention during the inspection process. Therefore, this paper proposes an intelligent inspection method for PV modules based on image processing and deep learning to improve the efficiency and accuracy of EL QC. The method pre-segments module images using EL image data acquisition and pre-classifies module types based on a priori defect types and then performs secondary detection of defective types of PV module defects using Faster RCNN. The proposed method’s effectiveness was verified by the EL images collected from an actual PV module production line. The algorithm model was able to label over 12 common defects with strong reliability and achieve a detection accuracy of over 98%. This greatly improves the efficiency and accuracy of EL detection of PV modules and reduces labour costs while improving the quality of PV module detection.