针对受光照不均影响的路面裂缝图像，提出一种基于Sobel算子和最大熵法的图像分割算法，并采用长线段与原图进行与操作和判断黑色像素所占比例的方法去除图像孤立噪声点.根据不同类型裂缝的几何形态，提取投影向量、分布密度和空洞数等特征值作为路面裂缝分类的依据，设计径向基函数神经网络的分类器实现对裂缝的准确分类.实验结果表明，较传统全局阈值算法，本文算法对光照不均图像的处理不仅能很好的提取裂缝边缘，且具有很强的抗噪能力，对路面裂缝的分类准确率高.

Compared with traditional global threshold algorithms,which can not extract the pavement crack exactly under non-uniform illumination,an improved image segmentation and classification method is proposed to overcome the drawbacks.Firstly,a new image segmentation method combining sobel and maximum entropy is presented and the solitary noise is reduced based on the pixel geometrical shape of the image.Secondly,a pattern classifier based on RBF neural network is designed to recognize different cracks according to the geometrical shape differences of different cracks.The experimental result indicates that compared with other image segmentation algorithms,the method proposed in this paper has a good ability to extract the image edge,and a strong ability to suppress the noise in the image.Moreover,it can achieve classification accuracy.