Work

Morphology-Adaptive YOLO for Underwater Crack Detection in Hydraulic Structures

Year: 2026

Type: article

Abstract: 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 [email protected] from 91.2% to 92.9% and [email protected]: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.

Source: Water

DOI: 10.3390/w18101241

Authors: Zhe Chen, Changning Zhou, Jingkun Guo, Guangjun Yin

Institutions: Hohai University, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China