Crack detection using spectral clustering based on crack features
Crack detection using spectral clustering based on crack features
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DOI：
10.1109/tencon.2016.7848502
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Taku Matsuoka;Kousuke Matsushima
中科院分区：
文献类型：
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作者：
Taku Matsuoka;Kousuke Matsushima
路面损伤检测对于道路的维护和管理具有重要意义，人们提出了多种基于路面图像的裂缝检测方法。然而，它们的准确性容易受到图像噪声的影响。本文将裂纹的连续性作为裂纹的特征之一引入到光谱聚类方法中，以避免噪声引起的误检。将连续性引入到亲和矩阵中。实验结果表明，该方法提高了检测精度。
Pavement distress detection has significant importance for maintaining and managing roads, and a variety of crack detection methods based on pavement images have been proposed. However, their accuracy is vulnerable to noise on images. In this paper, we introduce the continuity of cracks as one of the features of the cracks into a method using spectral clustering to avoid misdetection caused by noise. The continuity is introduced into affinity matrix. The result of experiment shows the accuracy gets higher by our approach.