NEWS ARTICLE
水务学术动态：Morphology-Adaptive YOLO for Underwater Crack Detection in Hydraulic Structures
论文标题：Morphology-Adaptive YOLO for Underwater Crack Detection in Hydraulic Structures
发布日期：2026-05-21
作者：Zhe Chen, Changning Zhou, Jingkun Guo, Guangjun Yin
DOI：10.3390/w18101241
论文摘要：Accurate underwater crack detection is essential for the condition monitoring of hydraulic structures. However, reliable detection in underwater inspection imagery remains challenging because of low visibility, complex backgrounds, large-scale variation, and irregular crack morphology. To improve detection under these conditions, we develop MA-YOLO, a YOLOv11-based detector that adapts feature representation to underwater crack morphology. The proposed method integrates a broader receptive field spatial pyramid pooling module to enhance multi-scale feature extraction, a morphological attention module to improve the representation of irregular crack patterns, and an extra-large detection head to better detect magnified cracks in close-range underwater images. Experiments on the underwater crack dataset (UCD) show that MA-YOLO outperforms both conventional detectors and recent underwater object-specific detectors. Relative to YOLOv11, MA-YOLO increases mAP@0.5 from 91.2% to 92.9% and mAP@0.5:0.95 from 60.0% to 63.0%, while maintaining a lightweight architecture and real-time inference capability. The results demonstrate the effectiveness of morphology-adaptive feature modeling for image-based underwater crack detection and its potential for practical monitoring of submerged hydraulic structures.
元数据：Crossref 收录的 MDPI Water 论文。 DOI: 10.3390/w18101241. Vol. 18, Issue 10. Authors: Zhe Chen, Changning Zhou, Jingkun Guo, Guangjun Yin.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/w18101241
PDF 链接：https://www.mdpi.com/2073-4441/18/10/1241/pdf