Panel-Aware Local Background Filtering for Photovoltaic Thermal Anomaly Detection and Automatic Bounding-Box...

Thermal imaging is widely used for identifying abnormal thermal patterns in photovoltaic (PV) systems. However, complex backgrounds, varying thermal conditions, and environmental factors can reduce the spatial reliability of thermal-anomaly localization, particularly under real operating conditions. This study proposes a training-free image-processing framework that integrates panel-centered analysis, inner-panel masking, panel-coverage control, and local-background filtering to detect thermal-anomaly candidates and automatically generate bounding-box pre-annotations for subsequent deep learning applications. The framework was evaluated using five images from a publicly available thermal PV dataset and four independently acquired UAV-based field images from a grid-connected rooftop PV system at Zonguldak Bülent Ecevit University (BEUN). Compared with global thresholding and a panel-constrained Otsu-based baseline, the proposed method generally reduced redundant detections and reduced the absolute number of out-of-region detections while retaining regions showing spatial agreement with the image-derived pseudo-reference. Evaluation on the pseudo-color field images demonstrated promising applicability under real operating conditions. Moreover, a clear domain shift and increased out-of-region detections in some images indicate that further improvement in robustness is required for pseudo-color thermal representations and complex real-world conditions. Therefore, the present results are regarded as preliminary proof-of-concept evidence rather than broad validation of robustness or field-deployment readiness. The framework is computationally lightweight and shows potential for near-real-time processing.