道路缺陷检测识别算法研究

发布时间：2018-01-06 06:39

本文关键词：道路缺陷检测识别算法研究　出处：《武汉工程大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着我国综合国力的增强,高速公路的蓬勃发展成为必然,路面养护也越来越得到重视。然而,人工作业效率低,精度也难以得到保障,车载式路面缺陷检测系统应运而生。在现场施工作业中,道路缺陷的检测尤为困难,如:坑槽、油污、阴影、路标等不可控因素,给检测带来很大的困难,现有算法难以保证检测的效率以及精度,通常导致需要深度修补的地段被忽略,路面缺陷检测算法的通用性成了技术瓶颈。针对上述难题,本文对路面缺陷图像的预处理、路面缺陷信息提取做了深入的研究。本文主要完成了以下工作:1、对采集的路面缺陷图像预处理。由车载式道路缺陷检测系统采集的路面缺陷图像,由于成像系统自身的缺陷,采集的图像灰度不均匀,针对该问题,本文对缺陷图像进行灰度校正,改善眩光造成的图像亮度、对比度不均匀问题。灰度校正之后,本文采用了一种改进的自适应滤波算法,有效的抑制了路面裂缝图像出现的噪声,而完整保留了路面裂缝的细节。2、路面裂缝信息的提取。完成图像的预处理工作之后,对其进行图像边缘提取以及图像分割处理,提出了基于非负特征的路面裂缝提取方法,在不改变原图像特征的基础上,采用基于背景标记的连通域去噪算法,快速遍历路面缺陷图像,减少了路面缺陷图像处理过程的复杂度,以便达到车载的要求。然后利用形态学变换提取膨胀结果的骨架,使图像更简易直观。再对其链码跟踪提取技术,计算裂缝的参数以便于形态描述,再利用面积和长度的阈值方法,删除短链,更好的表达裂缝信息,最后采用基于Zero梯度的轮廓提取算法,检测提取路面修补图像。在实验中,为保证算法的正确性,对每个步骤都做了验证,然后和当前的提取算法做了比较。实验结果表明:本文采用的图像采集装置能够弥补光照不足的影响,改进的路面缺陷图像预处理方法,有效的解决了图像眩光问题;提出的基于非负特征的路面缺陷提取算法,解决了路标、阴影以及油污的干扰;在高斯拉普拉斯算子的基础上,改进了算法,对路面修补图像的提取达到了良好的效果,能够满足道路养护中心实际检测周期的需要。
[Abstract]:As China's comprehensive national strength, will inevitably become the vigorous development of the expressway, road maintenance has received more and more attention. However, the manual operation efficiency is low, the precision is difficult to be guaranteed, as mobile pavement defect detection system. In the field of construction operations, road detection of defects is particularly difficult, such as pits, oil sign, shadows, and other uncontrollable factors, which brought great difficulties to the detection, the existing detection algorithm is difficult to ensure the accuracy and efficiency, usually leads to lots of depth repair is ignored, the general surface defect detection algorithm has become the technical bottleneck. To solve the above problems, pretreatment on the surface defect image, surface defect information the extraction is deeply researched. This paper mainly completed the following work: 1, on the surface defect image pretreatment collection. By road defect defect detection system of vehicle mounted road collection Because of the defect image, imaging system, image acquisition is not uniform, in order to solve this problem, the gray correction of defect images, improve image brightness glare caused by the problem of uneven contrast gray. After correction, this paper adopts an improved adaptive filtering algorithm, can effectively restrain the noise of pavement cracks in the image, and retained the integrity of the pavement crack details.2, extraction of pavement cracks information. After image processing, image edge extraction and image segmentation on the extraction method of pavement cracks is proposed based on non negative characteristics, based on the change features of original image, use connectivity domain denoising algorithm based on background markers, rapid traverse surface defect image, reduce the complexity of surface defect image processing process, in order to achieve the vehicle requirements. Then by using morphology Transform to extract the expansion results of the skeleton, make the image more simple and intuitive. The chain code tracking extraction technology, parameter calculation for crack morphology description, using threshold method and the length of the area, remove the short chain, the expression of fracture information better, finally using Zero gradient contour extraction algorithm based on the detection, extraction of pavement to repair the image. In the experiment, to ensure correctness of the algorithm, are validated on each step, then do the current comparison and extraction algorithm. The experimental results show that the image acquisition device used in this paper can compensate the effect of light is not enough, the improved surface defect image preprocessing method, effective solution the image extraction algorithm proposed glare problems; surface defect based on non negative characteristics, solve the signs, shadows and oil interference; based on Gauss Laplasse operator, improved algorithm for pavement repair The image extraction has achieved good results and can meet the needs of the actual detection cycle of the road maintenance center.

【学位授予单位】：武汉工程大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U418

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