中文摘要：

实际路面图像因噪声成分复杂、覆盖面广,给检测裂缝造成难度。针对路面病害中裂缝图像自身的特征,提出了一种裂缝自动检测算法。该算法首先使用灰度矫正和滤波处理对裂缝图像进行预处理,然后结合最大类间方差法和Canny算子对病害图像进行边缘检测,再基于裂缝图像中裂缝的最大连通性提出了一种检测定位和精确分割算法,最后利用卷积神经网络算法对路面裂缝分类识别。实验结果表明,该方法在路面裂缝检测效率上具有更大的优势,而且对于不同类型的裂缝图像都具有鲁棒性。

英文摘要：

The complexity of noises covers a wide area of actual road images which causes that it is difficult to detect cracks. An automatic pavement crack detection algorithm was proposed in view of the characteristics of crack image in pavement disease. Gray-scale correction and filtering was used to preprocess the crack image. The maximum interclass variance method and Canny operator were used to detect the edge of the disease image, and then the localization and accurate segmentation algorithm was proposed for the crack image based on the maximum connectivity of the crack in the fracture image. The convolution neural network algorithm was used to recognize the pavement cracks. The experimental results show that the proposed method is superior to other advanced algorithms on the crack detection efficiency, and robust to the different types of crack images.