Abstract Accurate detection of dam cracks from Unmanned Aerial Vehicle (UAV) imagery is crucial for structural health monitoring. However, prevailing methods face significant challenges in achieving a balance between the precise identification of minuscule cracks and computational efficiency when processing high-resolution images. To overcome these limitations, this paper proposes HiResDC-YOLO, a novel deep learning framework based on an enhanced YOLOv12 architecture. Our main contributions are threefold: First, we introduce an Adaptive Dynamic Transformer (ADyT) module to strengthen nonlinear feature representation and stabilize gradient flow during training. Then, we design a Multi-Scale Enhanced Detection (MSED) head that effectively utilizes shallow, high-resolution features to significantly improve the detection capability for fine cracks. Besides, we develop a Multi-Scale Convolutional Attention (MSCA) module to capture comprehensive contextual information across different scales by integrating deep convolutional layers and a multi-branch fusion mechanism. Furthermore, we propose a High-Resolution Adaptive Inference (HiResInfer) strategy, which utilizes region-guided slicing and feature caching to dramatically accelerate the inference speed on full-resolution images without compromising the integrity of small targets. Extensive experiments on a challenging self-collected UAV dam crack dataset demonstrate that HiResDC-YOLO achieves state-of-the-art performance, surpassing existing methods significantly in terms of precision, recall, and mean Average Precision (mAP), while maintaining high computational efficiency. This work presents a robust and practical solution for real-time dam inspection and engineering safety monitoring. The code and related resources are available at: https://github.com/lijin6/HiResInfer-YOLO.git .
Liu et al. (Mon,) studied this question.