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基于ARM嵌入式平台的车道线检测算法

关恬恬 杨帆

液晶与显示2024,Vol.39Issue(4):543-552,10.
液晶与显示2024,Vol.39Issue(4):543-552,10.DOI:10.37188/CJLCD.2023-0141

基于ARM嵌入式平台的车道线检测算法

Lane detection algorithm based on ARM embedded platform

关恬恬 1杨帆1

作者信息

  • 1. 河北工业大学 电子信息工程学院,天津 300401
  • 折叠

摘要

Abstract

Aiming at the problem that the existing lane detection algorithms are difficult to balance the detection accuracy and speed in practical application,a new lane detection algorithm based on ARM embedded platform is proposed.Firstly,a lightweight semantic segmentation network is designed.When SegNet structure is optimized,skip connections are added to the first layer of the network,and channel attention mechanism modules are added after every two convolutional layers to ensure detection accuracy and improve detection speed.Secondly,Kalman filter lane tracking model is constructed to improve the robustness of detection in video streams.Then,the encoder is reconstructed and the model is lightweight.The deep separable convolution is used instead of the traditional convolution to reduce the calculation cost and improve the detection speed.Finally,the Trt model is generated by TensorRT accelerated reasoning to facilitate its deployment in ARM embedded platform for real-time lane detection.Experimental results on the self-produced Tusimeple extended data set show that the proposed algorithm can cope with various complex traffic scenarios,and its detection accuracy is 98.03%,which is superior to other algorithms.And its detection speed reaches 50 FSP,which meets the real-time detection requirements.This algorithm has high robustness and good real-time performance in complex traffic scenarios,and has certain theoretical and practical value.

关键词

车道线检测/语义分割/深度可分离卷积/TensorRT加速/ARM嵌入式平台

Key words

lane detection/semantic segmentation/depth-separable convolution/TensorRT acceleration/ARM embedded platform

分类

信息技术与安全科学

引用本文复制引用

关恬恬,杨帆..基于ARM嵌入式平台的车道线检测算法[J].液晶与显示,2024,39(4):543-552,10.

基金项目

国家重点研发计划智能机器人专项(No.2019YFB1312102) (No.2019YFB1312102)

河北省自然科学基金(No.F2019202364)Supported by Intelligent Robot Special Project of National Key Research and Development Program(No.2019YFB1312102) (No.F2019202364)

Natural Science Foundation of Hebei Province(No.F2019202364) (No.F2019202364)

液晶与显示

OA北大核心CSTPCD

1007-2780

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