Lane Detection Based on Multi-Frame Image Input

Lane detection is one of the difficulties in implementing an advanced driving assistance system. In this paper, we show that the existing single frame-based algorithm suffers from the problem of unsatisfied detection result, which is directly caused by some extremely poor road environments (such as severe shadow occlusion and severe fading). To solve the problem, this paper proposes a multi-lane detection method based on the improved U-SegNet model. In order to get better feature extraction results, the number of network layers of the U-SegNet model is deepened, and a many-to-many structure is also proposed to improve the recognition rate of the algorithm. During the training stage, continuous multiple frames are input into the RNN module to get the feature maps for feature learning and prediction. The proposed method is tested on Caltech lane marking dataset, the results show that the proposed algorithm has good robustness and real-time performance, the multi-lane marking can be better detected under most complex road conditions, and the average detecting accuracy can achieve 96.95%.