DSSF-net: Dual-Task Segmentation and Self-supervised Fitting Network for End-to-End Lane Mark Detection

Abstract

Lane mark detection is one of the key tasks for autonomous driving systems. Accurate detection of lane marks under complex urban environments remains a challenge. In this paper, an end-to-end lane mark detection network named DSSF-net, which is capable of directly outputting the accurate fitted lane curves, is proposed. First, a dual-task segmentation framework for jointing lane category prediction and spatial partition is presented. An IoU-based loss function is put forward to tackle the severely imbalanced category distribution problem. Then a fully self-supervised curve fitting network is proposed to directly output the parameters of lane line upon the probability map. To achieve better accuracy, the fitting network is trained with two sub-stages: coarse regression and confidence-based optimization. Finally the entire DSSF-net is implemented end-to-end. Comprehensive experiments conducted on challenging CULane dataset show that our model achieves 74.9% in F1-score and outperforms the state-of-the-art models.