Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules

The efficiency of photovoltaic (PV) systems is often compromised by undetected faults, exacerbated by the complexity of thermal imagery backgrounds. This study presents a novel deep-learning-based approach to enhance fault detection in PV systems by customizing the Atrous Spatial Pyramid Pooling (ASPP) module within a U-Net architecture. We propose and evaluate three modified configurations U-Net-ASPP_Cent, U-Net-ASPP_Diag, and U-Net-ASPP_Hybrid each designed to address specific fault localization challenges, including central and diagonal fault patterns. These configurations aim to overcome the limitations of conventional U-Net-ASPP by enhancing multiscale feature extraction and improving segmentation accuracy in complex PV thermal images. The U-Net-ASPP_Hybrid configuration demonstrated the most balanced performance across all key metrics, achieving a 1.13% improvement in F1-score, a 3.01% increase in Intersection over Union (IoU), and a 9.86% reduction in loss compared to the baseline U-Net-ASPP. Additionally, the U-Net-ASPP_Cent and U-Net-ASPP_Diag configurations provided IoU gains of 1.18% and 1.96%, respectively, while also reducing false positive rates. These results highlight the effectiveness of incorporating region-specific dilation strategies, enhancing the model's ability to detect diverse and challenging fault patterns in complex thermal imagery. Beyond quantitative performance, qualitative segmentation analysis confirms that the U-Net-ASPP_Hybrid model offers superior fault localization and adaptability to real-world PV inspections. The U-Net-ASPP_Cent model is particularly effective for central anomaly detection, while the U-Net-ASPP_Diag model excels at identifying directional faults such as cracks. The U-Net-ASPP_Hybrid model, combining both strategies, provides a comprehensive solution for automated PV fault detection. These findings underscore the stability, scalability, and real-world applicability of the proposed models, making them ideal for automated PV inspection systems aimed at minimizing manual intervention and enhancing the reliability of renewable energy infrastructure. Future research will explore adaptive dilation strategies and more diverse datasets to further improve model generalization across varying PV environments.