基于分区域多尺度分析的路面裂缝检测算法

为了提高高速公路路面裂缝检测的准确性，提出一种基于分区域多尺度分析的新型路面缺陷检测算法，从图像的不同尺度上提取裂缝及其周围不同区域的灰度、熵和纹理特征分布信息，获得蕴含方向走势和弯曲程度等参数的特征向量，通过支持向量机(supportvectormachine，SVM)的学习并对所得特征向量进行判断，检测出裂缝点所在位置.实验结果表明，算法与其他路面裂缝检测算法相比，有效地提高了检测的抗噪性、通用性以及准确性，达到了理想的裂缝检测效果，满足公路质检的要求.

Pavement Crack Detection Algorithm Based on Sub region and Multi scale Analysis

In order to improve the accuracy of highway pavement crack detection， a new type of surface defects detection algorithm was proposed based on sub region and multi scale analysis. Gray， entropy and texture features distribution information were extracted in cracks and the surrounding areas from different scales of images. Feature vectors of parameters containing the direction trend and bending degree were acquired， and the crack location was detected through learning the support vector machine (SVM) and judging the eigenvector. Experimental results demonstrated that the resistance to noise， versatility and detection accuracy were improved effectively by the proposed algorithm in comparison to the other pavement cracks detection algorithms. The ideal crack detection effect was achieved， and the requirements of highway quality inspection were met effectively.