基于GSO-Kmeans的沥青路面裂缝分割算法研究

Research on crack segmentation algorithm of asphalt pavement based on GSO-Kmeans

传统的K-means聚类算法在进行图像分割时只考虑图像的特定灰度值,初始聚类中心的随机选取将导致分割结果存在很多干扰,在沥青路面这种高噪音的复杂背景下,裂缝的聚类提取效果不理想。本文提出了基于GSO-Kmeans算法来进行沥青路面裂缝分割。该算法首先使用GSO算法对沥青路面裂缝图像进行搜索,确定初始聚类中心,然后利用K-means聚类算法对沥青路面裂缝图像进行分割。结果表明,GSO-Kmeans算法在沥青路面裂缝提取方面有着很好的精准度,具有收敛速度快、分割结果准确等优势。

The traditional K-means clustering algorithm only considers the specific gray value of the image in image segmentation,and the random selection of the initial clustering center will lead to a lot of interference in the segmentation results.In the complex background of asphalt pavement with high noise,the clustering extraction effect of cracks is not ideal.In this paper,GSO-Kmeans algorithm is proposed to segment asphalt pavement cracks.The algorithm firstly uses GSO algorithm to search the asphalt pavement crack image and determine the initial clustering center,and then uses K-means clustering algorithm to segment the asphalt pavement crack image.The results show that GSO-Kmeans algorithm has good precision in asphalt pavement crack extraction,and has the advantages of fast convergence speed and accurate segmentation results.