To address the inability of traditional crack image segmentation methods to inaccurately extract the crack on the concrete surface, an improved lightweight global convolutional network crack image segmentation model is proposed in this study. Based on the principle of deep convolution network, the large convolution kernel is used to classify and locate crack images. For the characteristics of cracks, a lightweight semantic segmentation model MobileNetv2-GCN is constructed. Experimental results show that the MobileNetv2-GCN model delivers superior performance in three open crack datasets. The central axis skeleton algorithm is used to extract the crack skeleton subsequent to semantic segmentation, and the physical value of the average width of the crack is calculated. The proposed model has high accuracy and can provide reliable data support for road quality detection.