首页> 外文会议>Intelligent Networks and Intelligent Systems, 2009. ICINIS '09 >Automatic Road Crack Image Preprocessing for Detection and Identification
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Automatic Road Crack Image Preprocessing for Detection and Identification

机译:用于检测和识别的自动道路裂缝图像预处理

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Conventional visual and manual road crack detection method is no fit to the development of the highway: it is time-consuming, non-precise, dangerous, and costly. Automatic pavement survey is required. This paper treats a problem arising in the design of intelligent vehicles also describes an automatic road crack detection method based on image processing. It enhances the road image by comparing two fast correct method in the first, then processes segment threshold and extracts linear feature. It means that the diseases image is processed by Ostu algorithm and threshold division (by contrasting the whole pixel counter and statistics of the histogram) also we could set some parameters by experience and experimentation. Result shows that the Ostu algorithm makes more effective when there are some transverse cracks; longitudinal cracks or irregular cracks but is of no effect when there are just some noises. Threshold division makes much better by contrasting the whole pixel counter and statistics of the histogram. The experimental result is satisfactory.
机译:常规的视觉和手动道路裂缝检测方法不适合高速公路的发展:它耗时,不精确,危险且成本高。需要进行自动路面检查。本文针对智能车辆设计中出现的问题,提出了一种基于图像处理的自动道路裂缝检测方法。首先通过比较两种快速正确的方法来增强道路图像,然后处理路段阈值并提取线性特征。这意味着疾病图像是通过Ostu算法和阈值划分(通过对比整个像素计数器和直方图的统计数据)进行处理的,我们还可以通过经验和实验来设置一些参数。结果表明,Ostu算法在出现横向裂缝时更加有效。纵向裂缝或不规则裂缝,但是只有一些声音时才无效。阈值划分通过对比整个像素计数器和直方图的统计数据而变得更好。实验结果令人满意。

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