基于分数阶积分谷底边界检测的路面裂缝提取

Extraction of Pavement Cracks Based on Valley Edge Detection of Fractional Integral

针对路面裂缝图像噪声大、边界弱、裂缝细小导致分割难的问题,提出了一种基于谷底边界的路面裂缝提取方法.该方法首先对原始图像进行邻域平滑处理,在消除噪声的同时扩展了裂缝的相对宽度;接着对图像进行基于分数阶积分的谷底边界检测;然后采用形态学方法对图像进行处理,使裂缝趋于光滑后进行短线噪声去除,并结合最大熵阈值断线连接法自动连接裂缝断口,从而得到最终的裂缝检测结果.实验结果表明,该方法能快速检测出细小的路面裂缝,具有抗噪性能好、定位准确及检测精度高的特点.

As pavement crack images are difficult to segment due to the existence of high noise,weak boundary and small cracks,an extraction method of pavement cracks based on the valley edge detection of fractional integral is proposed.In this method,first,neighboring smoothing of the original image is performed to eliminate the noise and expand the relative width of the cracks.Then,the main cracks are extracted via the valley edge detection of frac-tional integral,and the resulting image is further processed via the morphological approach with short-line noise elimination.Afterwards,final cracks are extracted by using the gap linking method on maximum entropy threshold to cause cracks to merge automatically.Experimental results show that the proposed method instantly helps to detect small pavement cracks with high accuracy and strong noise robustness.