Localized Debris Detection in Post-Disaster Aerial Imagery Using YOLO-SDD

Abstract

Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with shadows, vegetation, roofs, vehicles, and damaged structures. This study proposes YOLO-SDD, a YOLO-based Shape-Guided Debris Detector built on YOLOv8 for localized debris identification in high-resolution post-disaster aerial imagery. YOLO-SDD combines a high-resolution P2 detection pathway with a shape-guided feature refinement module that uses box-supervised pseudo-mask and pseudo-boundary cues to refine P2-level features before final debris detection. A multi-event aerial imagery dataset was constructed from NOAA Emergency Response Imagery using images collected after hurricanes and a tornado in the United States. The model was evaluated using an image-level split, an event-level holdout test, component-level ablation studies, COCO-style scale-specific evaluation, and multi-seed stability analysis. On the image-level test set, YOLO-SDD achieved a precision of 0.959, recall of 0.933, mAP@50 of 0.970, and mAP@50:95 of 0.755, remaining competitive with larger YOLO-family models at lower computational complexity. In the event-level holdout test, YOLO-SDD achieved an AP@50 of 0.80 and an F1 score of 0.79, outperforming the YOLOv8s baseline and the selected large YOLO-family comparison model. The scale-specific evaluation showed improved AP@50 and recall for small and medium debris groups, while failure cases remained associated with shadows, vegetation, low contrast, and highly fragmented debris. The results indicate that shape-guided P2 refinement can improve localized debris screening under the tested conditions, although broader datasets, workflow integration, and human-in-the-loop validation are still needed before operational deployment.