ing at the problem that the conventional lane detection system uses a single-channel forward-looking camera under night scenes, which is susceptible to strong light interference and is prone to false detection and misdetection in complex scenes, we propose a lane detection method based on active infrared filter and around-view imaging. In the imaging stage, four-way vehicle-borne cameras based on active infrared filter are used to collect scene information around the vehicle, and then a look-around image with 360° overlooking effect is obtained based on perspective transformation and image fusion. In the detection phase of lane, a lane detection algorithm is proposed based on agglomerative hierarchical clustering. Firstly, based on the shape features of lane lines, a more pertinent template matching is designed to extract the edge points of the lane line. Then the edge points are clustered by agglomerative hierarchical clustering, and the lane is fitted by the random sample consensus algorithm. Finally, a priori information and Kalman filter are combined to further improve detection accuracy. The results show that the proposed algorithm can effectively eliminate the strong light effects during the detection of lanes and effectively reduce the false detection and missed detection rate to a certain extent.