关键词: 裂缝检测, 图像处理, 全卷积网络（FCN）, 平均交并比

Abstract: Cracks are one of the important diseases on the pavement surface. Traditional crack detection relies on manual visual inspection, which is time consuming and labor intensive. Although traditional image processing techniques can make crack detection and segmentation more automated to some extent. However, image processing techniques are susceptible to some noise caused by illumination, blur, and the like. In order to complete the segmentation and detection of pavement cracks in complex environments, a segmentation method based on improved Fully Convolutional Networks（FCN）is proposed. According to the established data set, the traditional FCN and the optimized FCN are trained. The test results show that the mean Intersection over Union（mean_IoU） is improved, so the proposed method can segment the cracks accurately.

Key words: crack detection, image processing, Fully Convolutional Networks（FCN）, mean intersection over union