Cracks are one of the most common types of imperfections that can be found in concrete pavement, and they have a significant influence on the structural strength. The purpose of this study is to investigate the performance differences of various spatial clustering algorithms for pavement crack segmentation and to provide some reference for the work that is being done to maintain pavement currently. This is done by comparing and analyzing the performance of complex crack photos in different settings. For the purpose of evaluating how well the comparison method works, the indices of evaluation of NMI and RI have been selected. The experiment also includes a detailed analysis and comparison of the noisy photographs. According to the results of the experiments, the segmentation effect of these cluster algorithms is significantly worse after adding Gaussian noise; based on the NMI value, the mean-shift clustering algorithm has the best de-noise effect, whereas the performance of some clustering algorithms significantly decreases after adding noise.
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机译:裂缝是混凝土路面中最常见的缺陷类型之一,它们对结构强度有重大影响。本研究旨在研究各种空间聚类算法在路面裂缝分割中的性能差异,为目前正在进行的路面维护工作提供一定的参考。这是通过比较和分析复杂裂纹照片在不同设置下的性能来完成的。为了评估比较方法的效果,选择了 NMI 和 RI 的评估指标。该实验还包括对嘈杂照片的详细分析和比较。根据实验结果,这些聚类算法在加入高斯噪声后分割效果明显变差;基于NMI值,均值移位聚类算法的去噪效果最好,而部分聚类算法在添加噪声后性能显著下降。
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