Axial attention-guided anchor classification lane detection

Lane detection is an important function of environment perception for autonomous vehicles. Although lane detection algorithms have been studied for a long time, existing algorithms still face many challenges in practical applications, mainly reflected in their unsatisfactory detection results when vehicles travel on roads with unclear or occluded lane lines such as in congestion, at night, or on curves. In recent years, deep learning-based methods have attracted more and more attention in lane detection because of their excellent robustness to image noise. These methods can be roughly divided into three categories: segment-based, detection-based, and parametric curve-based. Segmentation-based methods can achieve high-precision detection by detecting lane features pixel by pixel but have low detection efficiency due to high computational cost and time consumption. Detection-based methods usually convert the lane segments into learnable structural representations such as blocks or points,and then detect these structural features as lane lines. This method has the advantages of high speed and a strong ability to handle straight lanes, but their detection accuracy is obviously inferior to the segmentation-based methods. The performance of parametric curve-based methods lags behind well-designed segmentation-based and detection-based methods because the abstract polynomial coefficients are difficult for computers to learn. Following the framework of detection-based methods, a method that axial attention-guided anchor classification lane detection is proposed. The basic idea is to segment the lane into intermittent point blocks and transform the lane detection problem into the detection of lane anchor points. In the implementation process, replacing the pixel-by-pixel segmentation with a row anchor and column anchor can not only improve the lane detection speed but also improve the problem of missing visual cues of lane lines. In terms of network structure, adding the axial attention mechanism to the feature extraction network can more effectively extract anchor features and filter out redundant features, thereby improving the accuracy problem of detection-based methods. We conducted extensive experiments on two datasets, TuSimple and CULane, and the experimental results show that the proposed method has good robustness under various road conditions, especially in the case of occlusion. Compared with the existing models, it has comprehensive advantages in detection accuracy and speed. However as a detection method reliant on a single sensor, it remains challenging to achieve high-accuracy detection in highly complex real-world scenes, like rainy and polluted roads. Subsequent studies might achieve lane detection in more demanding environments by fusing multiple sensors together, such as laser radar and vision, and by incorporating prior constraints on vehicle motion.