Epurate-Net: Efficient Progressive Uncertainty Refinement Analysis for Traffic Environment Urban Road Detection

High-performance and real-time road detection plays an essential role in Advanced Driver Assistance Systems (ADAS) of intelligent transportation. However, existing approaches still suffer from ambiguous road contour in traffic environment because deep learning methods lack explicit constraints on road boundaries with similar textures and structures. To address the unsatisfactory boundaries, we propose an efficient architecture for urban road detection to refine road edges adaptively. First, we design a lightweight symmetrical data-fusion network to merge spatial responses into visual features. Second, we construct cross-layer attention transformation to aggregate non-local contextual information. Moreover, a progressive uncertainty analysis module eliminates indistinct road and obstacle edges. Finally, we introduce upgrade uncertainty loss and improved deep supervision to constrain margin error for multi-scale predictions. Results of experiments using three famous datasets confirm the superiority of our method (F1-measure of 96.91% in KITTI, 98.86% in Cityscapes, and 95.18% in R2D, processing speed of 0.02s) over previous approaches. We demonstrate that, to ensure the safety of autonomous driving, the Epurate-Net adaptively refines road contour to reach exquisite road margins. The source code will be available soon.