Shi Pengfei, Fan Xinnan, Ni Jianjun, Khan Zubair, Li Min
College of IOT Engineering, Hohai University, Changzhou, Jiangsu, China.
PLoS One. 2017 Jun 22;12(6):e0179627. doi: 10.1371/journal.pone.0179627. eCollection 2017.
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.
基于声纳图像的水下大坝裂缝检测与分类是一项具有挑战性的任务，这是因为水下环境复杂，且裂缝在性质上相当随机且多样。此外，可获取的声纳图像分辨率较低。为解决这些问题，提出了一种基于声纳图像的新型水下大坝裂缝检测与分类方法。首先，将声纳图像划分为图像块。其次，利用三维特征空间的聚类分析来获取裂缝片段。第三，使用改进的张量投票方法连接裂缝片段。第四，利用最小生成树来获取裂缝曲线。最后，提出一种结合模糊规则推理的改进证据理论对裂缝进行分类。实验结果表明，该方法能够在复杂水下环境下准确有效地检测水下大坝裂缝并对其进行分类。
