Occluded Lane Line Detection with Deep Polynomial Regression in Global View

Abstract

Occluded Lane line detection method based on depth polynomial regression in global field of view is proposed for the problem of lane lines being obscured on driving road. In order to obtain better lane line feature representation capability, a dual attention mechanism module that connects spatial attention and channel attention in series is introduced to improve the network's ability to obtain lane line features, and then its feature information is used to adopt the lane line detection method of line-direction position classification by adding a line-by-line detection branch after the VGG backbone network to search lane line pixel points through line-direction scanning; in order to distinguish the lane line In order to distinguish which lane line the pixel points belong to, a loss function is designed according to the idea of metric learning, and a vector block is introduced on the semantic segmentation network to record the vector distance of the lane line pixels; finally, the pixels on the current lane line are extracted by the OPTICS clustering model, and a depth polynomial approach is used to complete the fitting of the lane line. Experiments are conducted on the Tusimple dataset, and the results show that compared with the LaneNet network, the method in this paper improves 4.79% and 6.34% in accuracy and precision, respectively, and has a better detection effect on the obscured lane lines.