Lane Marking Regression From Confidence Area Detection to Field Inference

Lane marking detection is a fundamental yet challenging task for traffic scene understanding. Previous works generally predict traffic line segmentation and obtain lane marking coordinates with a post-processing step, which are vulnerable to various challenging environments including occlusion, illumination variation, shadow, background clutter. In this paper, we propose a novel method, named as Lane marking Regression Network (LRN), which can simultaneously consider confidence area detection and field inference for producing more precise lane markings in an unified encoder-decoder framework. For obtaining confidence detection of lane area, we introduce the graph diffusion mechanism to aggregate the contextual information for better detecting hard areas. The field inference considers the geometric relationship between lane area and lane markings by adopting a novel constraint regression function. Comprehensive evaluations on CULane and GD datasets well demonstrate the significant superiority of our proposed LRN over other state-of-the-arts for lane marking detection.