CNN based lane detection with instance segmentation in edge-cloud computing

Wei Wang,Hui Lin,Junshu Wang

doi:10.1186/s13677-020-00172-z

Abstract

At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

Highlights

With the advent of autonomous driving technology, people can largely get rid of the safety problems caused by daily manual driving
The difficulty in lane detection is how to deal with lane detection accuracy and real-time issues at the same time, so we need to improve the accuracy and efficiency of lane recognition between traditional and neural network-based lane recognition methods
We found that other models can achieve correspondingly good results when dealing with lanes in some scenes, but it is difficult to take into account all the scenes, while our proposed method is superior to other models in various scenarios, which is a benefit for lane recognition technology

Summary

Introduction

With the advent of autonomous driving technology, people can largely get rid of the safety problems caused by daily manual driving. Self-driving cars are sought after by many automobile consumers. Many researchers from academic institutions and industries have engaged in autonomous driving technology and these researches have promoted the development of image processing and computer vision technology. As a key part of the automatic driving system, lane detection technology is meaningful. The difficulty in lane detection is how to deal with lane detection accuracy and real-time issues at the same time, so we need to improve the accuracy and efficiency of lane recognition between traditional and neural network-based lane recognition methods.

Methods

Results

Conclusion