Automatic Lane Line Detection System Based on Artificial Intelligence

Abstract

As the need for an intelligent transport system is growing rapidly, lane line detection has gained a lot of attention recently. Aiming at the problem that the YOLOv3 algorithm has low accuracy and high probability of missed detection when detecting lane lines in complex environments, a lane line detection method for improving YOLOv3 network structure is proposed. The improvement is focused on detection speed and accuracy. Firstly, according to the characteristics of inconsistent vertical and horizontal distribution density of lane line pictures, the lane line pictures are divided into s ∗ 2S grids. Secondly, the detection scale is adjusted to four detection scales, which is more suitable for small target detection such as lane line. Thirdly, the YOLOv3’s backbone is changed by adopting Darknet-49 architecture. Finally, parameters of anchor and loss function are optimized so that they focus on detecting lane line. The experimental results show that on the KITTI (Karlsruhe Institute of Technology and Toyoko Technological Institute) dataset, the mean average precision value is 92.03% and the processing speed is 48 fps. Compared with other algorithms, it is significantly improved in detection accuracy and real-time performance. It is promising to employ the proposed approach in lane line detection system.