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基于计算智能的路面裂缝分割的几种聚类算法的比较与分析。

Comparison and Analysis of Several Clustering Algorithms for Pavement Crack Segmentation Guided by Computational Intelligence.

机构信息

School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China.

Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou, Duyun 558000, China.

出版信息

Comput Intell Neurosci. 2022 Sep 3;2022:8965842. doi: 10.1155/2022/8965842. eCollection 2022.

DOI:10.1155/2022/8965842
PMID:36097558
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9464106/
Abstract

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.

摘要

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。本研究旨在探讨各种空间聚类算法在路面裂缝分割中的性能差异，为当前的路面维护工作提供一些参考。这是通过比较和分析不同设置下复杂裂缝照片的性能来实现的。为了评估比较方法的效果，选择了 NMI 和 RI 评价指标。实验还包括对噪声照片的详细分析和比较。根据实验结果，这些聚类算法在添加高斯噪声后分割效果明显变差；基于 NMI 值，均值漂移聚类算法具有最佳的去噪效果，而一些聚类算法在添加噪声后性能明显下降。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/e390fa08d1f7/CIN2022-8965842.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/36c806322bdd/CIN2022-8965842.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/4f9a085cfee7/CIN2022-8965842.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/03d9cb9951ae/CIN2022-8965842.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/907a97760331/CIN2022-8965842.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/e33edb2e2ae5/CIN2022-8965842.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/5b0a2ee05958/CIN2022-8965842.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/e390fa08d1f7/CIN2022-8965842.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/36c806322bdd/CIN2022-8965842.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/4f9a085cfee7/CIN2022-8965842.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/03d9cb9951ae/CIN2022-8965842.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/907a97760331/CIN2022-8965842.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/e33edb2e2ae5/CIN2022-8965842.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/5b0a2ee05958/CIN2022-8965842.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a49/9464106/e390fa08d1f7/CIN2022-8965842.007.jpg

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本文引用的文献

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Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation.基于聚类分析的 MRI 脑组织分割应用。
Comput Math Methods Med. 2022 Aug 3;2022:7401184. doi: 10.1155/2022/7401184. eCollection 2022.
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Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module.基于带有 scSE 注意力机制模块的深度特征聚合分割网络的自动像素级路面裂缝识别。
Sensors (Basel). 2021 Apr 21;21(9):2902. doi: 10.3390/s21092902.
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Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder.
基于深度自动编码器的改进型像素级路面缺陷分割。
Sensors (Basel). 2020 Apr 30;20(9):2557. doi: 10.3390/s20092557.
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A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation.一种具有共享跨域转移潜在空间的新型抗负迁移模糊聚类模型及其在脑CT图像分割中的应用
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