Most traditional lane detection algorithms rely on the combination of handcrafted features and heuristic algorithms, which are easily affected by factors such as vehicle occlusion and ground fouling. Aiming at the complicated problems that affect lane line detection, this paper considers lane line detection as a continuous segmentation problem, and proposes a lane line detection method based on dense segmentation network. To this end, dense blocks are used to construct a dense segmentation network (DSNet), so DSNet can reuse features to improve the performance of extracting lane line instance features and restoring the feature map resolution. At the same time, the proximity AND operation and Meanshift clustering algorithm are also introduced to process the output of DSNet, which reduces the influence of non-lane line pixels and makes the boundary of detection results more specific. Experiments show that the proposed algorithm can well solve the problem of vehicle occlusion and ground fouling, and can also determine the number of lane lines, which has better robustness and real-time performance.