Automated damage detection and assessment for underwater bridge structures using multi-scale image fusion enhancement and deep learning
The integrity and safety of underwater bridge structures can be compromised by damage; therefore, timely detection and assessment are crucial. However, underwater damage detection is constrained by turbidity, low illumination, and multiple coexisting damage types, which complicates comprehensive automated safety assessment. This study proposes an automated framework for structural damage detection and safety assessment of underwater bridge structures. Multiple types of underwater damage are analyzed, and an underwater damage image dataset (UDID) is established. A modified linear unsharp masking method is used to adaptively enhance the high-frequency features of the UDID through multi-scale image fusion. A well-trained GoogLeNet model is used to automatically detect underwater structural damage. Based on the detection results, bridge safety is classified into five levels using the damage index method. A case study involving a concrete bridge in China demonstrates the effectiveness of the proposed framework. The GoogLeNet model achieves a detection accuracy of 97% in the large-scale test. The concrete bridge is assessed as level 3, which represents moderate damage and is consistent with field detection results. In underwater environments featured by a low signal-to-noise ratio, the detection accuracy of damage types that depend on texture and contrast features decreases significantly. This framework effectively addresses the limitations of existing underwater damage detection methods and enables automated safety assessment for underwater bridge structures.