福州大学学报（自然科学版）2026，Vol.54Issue(1)：51-58,8.DOI:10.7631/issn.1000-2243.25060
改进Murphy证据理论的水下桩墩病害声呐图像融合检测
Improved Murphy evidence theory for sonar image fusion detection method of underwater pier defect
摘要
Abstract
Aiming at the issues of low resolution,significant noise interference in sonar images,and insufficient accuracy of single data sources in submerged pile structure defect detection,an improved Murphy evidence theory-based multi-source sonar image fusion detection method is proposed.Firstly,a complementary model based on Darknet-53 and ResNet-50 is constructed to provide diversified evidence and strengthen information representation.Secondly,the squeeze-excitation(SE)attention mechanism is integrated into the two complementary models to enhance the detection performance for defect areas.Finally,the Murphy evidence theory is modified to facilitate data fusion,thereby improving the accuracy and robustness of the method.Experimental results show that the proposed method outperforms other models,with precision,recall,and average precision all exceeding 93.7%for detecting common defect types.These results demonstrate that the improved Murphy evidence theory,integrated with deep learning and multi-source data,significantly improves the accuracy and environ-mental adaptability of sonar image defect detection,offering an innovative solution for defect identifica-tion in complex underwater engineering projects.关键词
水下桩墩/病害检测/声呐图像/Murphy证据理论/激励网络(SE)注意力机制/特征提取Key words
underwater pile foundations/disease detection/sonar imagery/Murphy evidence theory/squeeze-excitation(SE)attention mechanism/feature extraction分类
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王圣贤,姜绍飞,王威,苏振恒..改进Murphy证据理论的水下桩墩病害声呐图像融合检测[J].福州大学学报（自然科学版）,2026,54(1):51-58,8.基金项目
福建省自然科学基金重点资助项目(2022J02016) （2022J02016）
福建省自然科学基金资助项目(2022J01969) （2022J01969）
