Gate-Conv SVDD: An Anomaly Detection Framework for Fault Inspection of Photovoltaic Panels Using UAVs

Anomaly detection in solar photovoltaic panels using uncrewed aerial vehicles (UAVs) faces challenges due to minimal texture variations from surface anomalies (e.g., shadows, eddy currents) in UAV-captured imagery, which constrain both detection precision and real-time performance. Existing approaches often lack quantitative anomaly analysis that integrates aerial imagery with operational data, thereby limiting their practical value and impeding sustainable industry advancement. To address these limitations, we propose gate convolution support vector data description (Gate-conv SVDD), a novel framework that enhances the efficiency and accuracy (ACC) of anomaly detection through rapid parallel gated feature compression and hypersphere-based support vector data description (SVDD) clustering. This approach enables precise anomaly localization in high-resolution UAV imagery, as validated through simulations and controlled experimental datasets. Gate-conv SVDD further supports quantitative assessments of anomaly severity, thereby bridging the gap between image-based detection and actionable analysis. Designed for computational efficiency, the framework demonstrates strong potential for fast inference in support of real-time UAV imaging, subject to further hardware integration and field validation. Extensive experiments demonstrate that Gate-conv SVDD outperforms state-of-the-art methods, offering superior ACC and robustness in controlled settings.