Lane detection algorithm based on improved UFLD

The traditional lane detection methods often have the problems of large calculation and low detection efficiency, and their detection accuracy will be seriously affected in complex scenes such as poor lighting conditions, lane damage or occlusion. The UFLD(Ultra Fast Lane Detection) algorithm solves the above problems well. However, UFLD algorithm has some problems, such as weak feature extraction ability, and it can't handle context information well. This paper proposes a module combining Split-Attention mechanism and adaptive spatial feature fusion(ASFF), which not only improves the expression ability of features, but also can selectively learn high-level semantic information to avoid the suppression of detail information by background or global context information, thus improving the model's ability to understand and segment images. The experimental results of ablation show that the detection speed of the improved algorithm is equivalent to that of the original one, and the detection effect is improved by 2.3 and 0.16% compared with the original UFLD algorithm on the data sets of CULane and TuSimple, and the robustness and generalization ability of the improved algorithm are improved.