Adaptive Multi-Lane Detection Based on Robust Instance Segmentation for Intelligent Vehicles

Recent advances in lane detection based on deep neural networks have enhanced the detection performance of intelligent vehicles under different traffic scenes. However, it’s still difficult for existing lane detection algorithms to robustly extract lane instances and adapt to varying lane numbers simultaneously. In this paper, we focus on anchor-free methods and propose an adaptive multi-lane detection approach based on instance segmentation for intelligent vehicles. By combining instance segmentation with lane center estimation, the proposed approach achieves robust and efficient performance. Besides, cosine-metric is incorporated into the objective functions that enables the proposed method to extract more discriminative features for foreground extraction. Different from the proposed approach, most existing algorithms treat the lane instance detection task as a multi-class object detection problem which require to predefine different lane categories and fix the lane number in advance, while the lane category predefinition degrades the robustness of the algorithms. Experimental results on public datasets demonstrate that the proposed approach achieves better overall performance compared with the state-of-the-art anchor-free lane detection methods. It is also illustrated that the proposed approach can be easily extended to other instance segmentation tasks, e.g. vehicle segmentation.