A novel underwater dam crack detection and classification approach based on sonar images

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.基于声纳（sonar）图像的水下大坝裂缝检测与分类是一项极具挑战性的任务，原因在于水下环境复杂多变，且裂缝本身兼具随机性与多样性。此外，可获取的声纳图像普遍分辨率较低。为解决上述问题，本文提出一种新颖的基于声纳图像的水下大坝裂缝检测与分类方法。首先，将声纳图像划分为若干图像块；其次，通过对三维特征空间开展聚类分析以提取裂缝片段；随后，采用改进的张量投票法对裂缝片段进行连接；第四步，借助最小生成树算法得到裂缝曲线；最后，提出一种结合模糊规则推理的改进证据理论以实现裂缝分类。实验结果表明，所提方法能够在复杂水下环境中准确且高效地完成水下大坝裂缝的检测与分类任务。