Lane line detection plays a pivotal role in autonomous driving and advanced assisted driving. However, traditional lane line detection technology was less robust, and most methods based on deep learning were more complex and difficult to embed platform real-time application. A lightweight lane line detection network for embedded platforms was proposed, which converts lane line detection into a semantic segmentation problem. The network draws on U-Net and Segnet network structures, and uses small-scale convolution and other lightweight components to design and calculate efficiently semantic segmentation network. Based on the detection of the lane line, calculate the distance between the vehicle and the lane line on both sides, as well as the curvature of the lane line, and give an early warning when the vehicle deviates from the lane line or the detection was abnormal. Finally, the entire system was transplanted to the HiSilicon platform. Experimental results show that the system has high detection accuracy and detection speed, the accuracy rate reaches 97.5%, the speed reaches 50 FPS, and meets real-time requirements.Therefore, the system can be used for real-time lane line detection, ranging, and distance measurement for embedded platforms. Curvature calculation and early warning.