As a key technology in intelligent driving field, lane detection plays an important role in advanced driver assistant system (ADAS), which includes lane departure warning (LDW) and lane keeping (LK), lane changing (LC) and forward collision warning (FCW), adaptive cruise control (ACC). The visionbased method is dominant in the research on lane detection technology, which is also the future development direction. This paper reviews the research progress in lane detection methods based on vision in recent twenty years. Firstly, the classification and characteristics of lane are briefly described. The general process of lane detection and its faced challenges are clarified. On this basis, the lane detection principle of the lane detection methods, including the featurebased method, modelbased method, learningbased method and etc. are emphatically expounded. Their advantages and disadvantages are reviewed, analyzed and compared. Then, the commonly used datasets and the performance evaluation indexes for lane detection are introduced. Finally, aiming at the current existing problems of lane detection, the further research direction is prospected.