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:2019,28(2):177-183
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基于图像内容的沥青路面病害区域分割算法
(重庆交通大学 信息科学与工程学院, 重庆 400074)
Asphalt Pavement Image Region Segmentation Algorithm Based on Image Content
(School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
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投稿时间:2018-08-19    修订日期:2018-09-18
中文摘要: 针对CCD采集的沥青路面病害图像分辨率过高且含信息的有效区域占比少的问题,提出一种基于图像内容的沥青路面病害图像区域分割算法,以剔除路面图像中的无效区域.首先通过预处理和病害提取过程将原图像处理成包含病害特征的二值图像;然后通过计算整幅图像中含信息像素的上下比和左右比,得到初始化遍历方向,并统计各行(或列)的含信息像素总数;最后从初始化遍历方向开始遍历并丢弃含信息量最少的行(或列),最终得到分割后的图像.为验证算法的有效性与合理性,采用图像信息熵作为算法评价标准与传统算法进行对比分析.实验结果表明:本文算法在有效降低图像分辨率的前提下能很好的保留病害目标信息,提升图像信息熵.
Abstract:The resolution of the asphalt pavement disease image which collected by the CCD is too high and the area of the effective area containing information is small. A region segmentation algorithm for asphalt pavement disease image based on image content was proposed to eliminate the invalid region in the pavement image. Firstly, the original image was processed into a binary image containing the disease characteristics through a process of preprocessing and disease extraction. Then the initial traversal direction is obtained by calculating the up-to-down ratio and the left-to-right ratio of the pixels which contain information in the whole image, and counting the total number of information pixel of each row (or column). Finally, traversing from the initial traversal direction and discarding the row (or column) with the least amount of information in order to finally obtain the segmented image. In order to verify the validity and rationality of the algorithm, the image information entropy was used as the algorithm evaluation standard and compared with the traditional algorithm. The experimental results show that the proposed algorithm can keep the target information very well on the premise of effectively reducing the image resolution, and improve image information entropy.
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基金项目:重庆市教委科学技术研究项目(KJQN201800716)
引用文本:
蓝章礼,黄涛,李战,匡恒.基于图像内容的沥青路面病害区域分割算法.计算机系统应用,2019,28(2):177-183
LAN Zhang-Li,HUANG Tao,LI Zhan,KUANG Heng.Asphalt Pavement Image Region Segmentation Algorithm Based on Image Content.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):177-183

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