Boost-Up Efficiency of Defective Solar Panel Detection With Pre-Trained Attention Recycling

Methods that enable the visual inspection of solar panels are currently in demand, as a huge number of solar panels are now being deployed as a sustainable energy source. One of the solutions for inspection automation is an end-to-end deep learning framework, but this is not recommended for this problem because such a framework requires not only powerful computational resources, but also a large-scale class-balanced dataset. In this study, we present a cost-effective solar panel defect detection method. We emphasize the spatial feature of defects by utilizing an attention map that is generated by a pre-trained attention mechanism that can give attention on stroke ends, gathering, and bends. We define and extract 13 statistical features from the attention map, and then feed them into conventional machine learning model. Therefore, we no longer require energy depleting models such as end-to-end neural classifiers to discriminate between non-defective and defective panels. Five conventional machine learning models and one state-of-the-art (SOTA) deep learning model—i. e., EfficientNet—are used to generalize the experimental results. The results of the comparative experiments indicate that our approach, which includes attention mechanism recycling and statistical feature extraction, is guaranteed to provide cost-effective defect detection in general with performance that is competitive with that of recent SOTA. In future research, we expect that our approach can be adopted in other defect detection tasks such as steel or film manufacturing processes.