A Multi-Generational YOLO Ensemble with Weighted Boxes Fusion for Robust Rescue-Oriented Object Detection in Chaotic Disaster Scenes

Abstract

Accurate and robust object detection in complex disaster scenes is essential for effective emergency response; however, severe occlusion, dense overlap, and cluttered backgrounds pose significant challenges to conventional single-model detectors. To address these limitations, this study proposes a novel rescue-oriented detection framework that integrates a fine-grained disaster dataset, a cross-generational YOLO ensemble, and a consensus-based fusion strategy using Weighted Boxes Fusion (WBF). A dataset of 2323 images was constructed by re-annotating CDNIC19k with instance-level labels for four rescue-critical roles, enabling more precise evaluation in real-world scenarios. Heterogeneous YOLO models spanning multiple architectural generations were jointly exploited within a unified ensemble framework to leverage complementary representations. Meanwhile, a consensus-driven fusion strategy based on WBF was adopted to improve prediction aggregation in dense and occluded scenes. Experimental results showed that the proposed method outperformed single-model baselines and NMS-based approaches, improving mAP@0.5 from 0.696 to 0.756 (+6.0%) while maintaining strong recall and robustness. Analysis of the YOLOv12 family reveals an accuracy–efficiency trade-off, where lightweight models enable real-time inference while high-capacity models provide more reliable detection. Overall, these findings demonstrate that cross-generational architectural diversity combined with consensus-based fusion constitutes a generalizable and effective paradigm for high-precision disaster scene understanding under diverse deployment constraints.