Aiming at the contradiction between high cost and high precision in the field of road crack detection and quantification, this paper proposes a low cost, high precision automatic segmentation and quantification system for road cracks. Firstly, the convolutional neural network with jump-stage round-trip multi-scale fusion module and attention gate mechanism is used for segmentation prediction, which is named SW-Net. Then, the cracks are classified by combining MCO, DFS and the trend of pixel statistical curves in different directions. Finally, in order to overcome the discontinuity of crack quantization and the limitation of traditional morphological skeleton quantization algorithm, this paper combined the A* algorithm and extended it to calculate the shortest length and maximum width of cracks. Experimental comparison results show that the system achieves the best accuracy (93.68%) and F1 score (0.896 5) among all comparison models on the Crack500 dataset. The average classification accuracy of the improved classification algorithm is 99.29%, and the classification speed is 109 pieces/s. The relative errors of the shortest length and maximum width are 12.34% and 15.85% respectively, which is 5.16% lower than the average error of the traditional skeleton method. These results show that the system has made remarkable progress in the segmentation, classification and quantification of cracks.