A Network That Balances Accuracy and Efficiency for Lane Detection

Ce Zhang,Yu Han,Yier Lin,Dan Wang,Wei Qiao,Fazlullah Khan

doi:10.1155/2021/1099434

Abstract

In the automatic lane-keeping system (ALKS), the vehicle must stably and accurately detect the boundary of its current lane for precise positioning. At present, the detection accuracy of the lane algorithm based on deep learning has a greater leap than that of the traditional algorithm, and it can achieve better recognition results for corners and occlusion situations. However, mainstream algorithms are difficult to balance between accuracy and efficiency. In response to this situation, we propose a single-step method that directly outputs lane shape model parameters. This method uses MobileNet v2 and spatial CNN (SCNN) to construct a network to quickly extract lane features and learn global context information. Then, through depth polynomial regression, a polynomial representing each lane mark in the image is output. Finally, the proposed method was verified in the TuSimple dataset. Compared with existing algorithms, it achieves a balance between accuracy and efficiency. Experiments show that the recognition accuracy and detection speed of our method in the same environment have reached the level of mainstream algorithms, and an effective balance has been achieved between the two.

Highlights

In automatic lane-keeping system (ALKS), the vehicle must reliably detect the boundary of its current lane for precise positioning, and on this basis, the understanding of the traffic scene is completed, and the vehicle is kept in the lane through trajectory planning and vehicle control
To effectively balance the accuracy and efficiency of the algorithm, we propose a single-step lane detection method based on the MobileNet v2 + spatial CNN (SCNN) network. e network backbone of the model adopts the lightweight MobileNet v2 [10], which can effectively reduce the calculation amount and parameters of the lane model
We propose a single-step lane detection method based on the MobileNet v2 + SCNN network to solve the balance between accuracy and efficiency. e backbone network is based on the lightweight MobileNet v2, which greatly reduces the amount of calculation and parameters of the lane model

Summary

Introduction

In ALKS, the vehicle must reliably detect the boundary of its current lane for precise positioning, and on this basis, the understanding of the traffic scene is completed, and the vehicle is kept in the lane through trajectory planning and vehicle control. The lane detection module is the starting point of the entire system, and its safety and effectiveness are important. Is type of method achieves high accuracy by automatically extracting features from data, but the low-efficiency decoder makes the calculation efficiency low and is not sensitive enough to the curve scene [5, 6]. In response to this situation, the method based on message transmission [7, 8] uses spatial information in deep neural networks to capture the global context

Methods

Results

Conclusion