Abstract: : In order to improve the efficiency and accuracy of asphalt pavement disease recognition based on image processing, the multi-scale retinex(MSR) algorithm belonged to image enhancement processing was utilized to reduce the factors that seriously affect pavement disease image quality, such as uneven illumination and changeable road scenes; To solve the problem that the SegNet was difficult to accurately segment the fine defects on asphalt pavement, the residual network (ResNet) with better effect than visual geometry group network (VGG) was used as the backbone network, simultaneously, the dilated convolutional layers were employed to improve the recognition performance of the improved network for small diseases; Aiming at the issues that the improved network has a high false detection rate when recognizing diseases, the threshold method was used to eliminate false positives in the segmentation results. In order to verify the effectiveness of the improved network, it was compared with the representative semantic segmentation methods (such as SegNet, BiSeNet) on the same dataset, and the mean intersection over union (MIoU) and F1 scores (F1) of the three were (77.6%, 89.9%), (67.4%, 87.4%), (69.7%, 89.8%), respectively. The proposed algorithm was used to segment asphalt pavement sealed cracks in some road sections in Gansu Province. Compared with manual detection, the missed detection rate and the false detection rate were 0.09%, 2.49%, respectively. The experimental results show that the proposed method can segment the asphalt pavement sealed cracks more accurately.