A Deep Learning-Based Monitoring Framework for Foreign Object Detection in Power Distribution Substations

With the increasing adoption of unattended power distribution substations, accurate foreign object detection has become critical to ensure safe system operation. This study proposes a detection model tailored for substation monitoring, targeting hazards such as fire, water accumulation, and small animal intrusion, while accounting for varying on-site illumination conditions. First, an adaptive illumination normalization module is introduced to accommodate diverse lighting conditions, thereby enhancing its capability to capture foreign objects under complex illumination environments. Second, a multi-scale feature extraction and attention-based refinement structure is developed to effectively capture foreign objects with diverse sizes and textures, aligning with the specific detection requirements of substation scenarios. Third, a task-oriented loss function is constructed by incorporating illumination-adaptive weighting into the objectness component, thereby enhancing robustness under uneven illumination conditions. Experimental results demonstrate that the proposed method outperforms representative detection approaches, validating its effectiveness for foreign object detection in substation monitoring applications.