Lane detection is a challenging task due to the diversity of lane lines and the complexity of traffic scenes. The detection results of the existing detection methods are not ideal when the vehicle is driving in congestion, at night, and the lane lines are not clear or blocked on the road such as curves. Based on the framework of detection methods, a method that axial attention-guided anchor classification lane detection is proposed to solve two problems. The first is the problem of missing visual cues when lane lines are unclear or missing. The second problem is the lack of feature information caused by using sparse coordinates on mixed anchors, which leads to a decline of detection accuracy. Therefore, an axial attention layer is added to the backbone network to focus on prominent features of the row and column directions to improve the accuracy. Extensive experiments are conducted on the TuSimple and CULane datasets. Experimental results show that the proposed method is robust under various conditions while showing comprehensive advantages in terms of detection accuracy and speed compared with existing advanced methods.