适于路面破损图像处理的边缘检测方法

根据路面图像存在的背景不均、损坏比例低和损坏方向不规则的问题,提出了适于路面损坏图像处理的边缘检测方法.在传统边缘检测方法的基础上,引入了预处理和边缘增强.其中预处理包括背景校正、高斯平滑、灰度直方图变换,并提出有效灰度区间的概念;边缘增强则采用了数学形态学的膨胀运算和中值滤波.针对路面损坏图像实例,采用8方向Sobel算子和最大类间方差分割算法,按照上述流程进行边缘检测.结果表明,该方法能有效降低噪声对路面图像处理的影响并最大限度地保留图像中的损坏特征,而背景校正和基于有效灰度区间的灰度直方图变换则是该方法的关键.对经过预处理的边缘图像,最大类间方差法可取得理想的分割效果.

An Edge Detection Method Designed for Pavement Distress Images

An edge detection procedure designed for pavement images was presented in this study to overcome the difficulties in traditional methods of edge detection for pavement images such as nonuniform background illumination, low proportion of distress pixels and irregular directions of distress. The procedure offered in this study introduced image preprocessing and edge enhancement to traditional edge detection methods. The image preprocessing was constitute by background correction, Gaussian smoothing and histogram transformation based on the conception of effective range of gray levels which was proposed by this study for the first time, while the edge enhancement included the morphological dilation and median filtering operation. A pavement distress image was tested by Sobel detector of eight directions and Ostu method following the procedure above. The result showed that the procedure presented in this study can dramatically dampen the impact of noise on edge detection, and the background corrections as well as the histogram transformation based on effective range of gray levels are the key components of the procedure. Besides, Ostu method can provide good segmented edge maps if the images have been preprocessed.