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作 者:邱延峻[1,2] 王国龙 阳恩慧[1,2] 余孝丽[1,2] 王郴平[1,2,3] QIU Yanjun;WANG Guolong;YANG Enhui;YU Xiaoli;WANG Chenping(School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;Highway Engineering Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu 610031,China;School of Civil and Environmental Engineering,Oklahoma State University,Stillwater OK74078,USA)
机构地区:[1]西南交通大学土木工程学院,四川成都610031 [2]西南交通大学道路工程四川省重点实验室,四川成都610031 [3]俄克拉荷马州立大学土木与环境工程学院,俄克拉荷马静水OK74078
出 处:《西南交通大学学报》2020年第3期518-524,共7页Journal of Southwest Jiaotong University
基 金:国家自然科学基金(U1534203,51478398)。
摘 要:针对由裂缝对比度低、路面纹理复杂多变等因素引起的沥青路面三维图像的裂缝检测精度低的问题,对原始三维裂缝图像进行尺寸降维、灰度校正、高斯滤波等预处理;然后以图像截面为研究对象,分别对4个方向的截面依次进行特别设计的倾斜度、高斯分布、边缘梯度3种特征检验,从而获得裂缝截面;接着对各个方向的裂缝截面进行融合和去噪,获得完整的裂缝二值图像;最后,根据路面粗糙度的高低,变化高斯分布特征检验中的相关参数,实现裂缝的高精度检测.研究结果表明:提出的算法能达到89.19%的准确率、93.69%的召回率及91.06%的F值,优于基于三维光影、种子识别的典型三维图像裂缝检测方法.In order to solve the accuracy problems in the crack detection of 3D asphalt pavement,which are mainly caused by low contrast between cracks and the surrounding area and complex pavement textures,a threestep preprocessing was conducted on original 3D images firstly,including size reducing,intensity correction and Gaussian smoothing.Then,three predominant feature tests of tilt-level,Gaussian-distribution and edge-gradient were applied to the image profiles of four directions successively so as to obtain the crack profiles.Moreover,the crack profiles of four directions were merged and denoised to acquire the intact cracks.Finally,according to the roughness of pavement surface,a related parameter in the Gaussian-distribution test was adjusted to realize the crack detection of high accuracy.The experiment result indicates that the proposed algorithm can reach 89.19%of accuracy,93.69%of recall and 91.06%of F-measure,which outperforms another two typical 3D recognition algorithms based on the theories of 3D shadowing and crack seeds.
关 键 词:道路工程 识别算法 图像处理 路面裂缝 多特征 三维图像
分 类 号:U416.2[交通运输工程—道路与铁道工程]
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