AHD‐YOLO: An Adaptive Hybrid Dynamic Network for Building Damage Detection

ABSTRACT Current deep learning‐based building damage detection methods often suffer from limited accuracy and high computational complexity. To address these limitations, we propose a novel framework based on the YOLOv11 architecture, termed adaptive hybrid dynamic YOLO (AHD‐YOLO). AHD‐YOLO introduces three key innovations. Omni‐dimensional dynamic fusion (ODFusion) enhances the adaptability and precision of feature extraction. Adaptive in‐scale feature interaction (AIFI) captures fine‐grained damage features. Adaptive high‐level screening feature fusion pyramid network (AHSFPN) emphasizes critical damage regions while maintaining a lightweight design. Experiments conducted on the building damage dataset show that AHD‐YOLO achieves 70.5% mAP, 60.2% Recall, and 48.2% mAP@0.5:0.95, representing respective improvements of 2.1%, 1.3%, and 1.7% over YOLOv11s. Moreover, the model also reduces the number of parameters and GFLOPs by 11.0% and 13.0%, respectively. Comparative experiments indicate that AHD‐YOLO outperforms current state‐of‐the‐art detection methods. In generalization tests on a structural damage dataset, the model achieves 78.3% detection accuracy, exceeding YOLOv11s by 2.7%. These results confirm that AHD‐YOLO effectively balances detection precision and computational efficiency, enabling accurate and real‐time identification of multiple damage types in practical building inspection scenarios.