基于ARM嵌入式平台的车道线检测算法  

Lane detection algorithm based on ARM embedded platform

作　　者：关恬恬 杨帆 GUAN Tiantian;YANG Fan(College of Electronic Information Engineering,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学电子信息工程学院,天津300401

出　　处：《液晶与显示》2024年第4期543-552,共10页Chinese Journal of Liquid Crystals and Displays

基　　金：国家重点研发计划智能机器人专项(No.2019YFB1312102);河北省自然科学基金(No.F2019202364)。

摘　　要：针对现有车道线检测算法在实际应用中难以平衡检测精度和速度的问题,提出一种全新的基于ARM嵌入式平台的车道线检测算法。首先,设计一个轻量化语义分割网络,在优化SegNet结构的同时在网络第一层加入跳跃连接,并且在每两个卷积层后加入通道注意力机制模块,在保证检测精度的同时提升检测速度。接着,构建卡尔曼滤波车道线跟踪模型,提高检测在视频流中的鲁棒性。然后,重构编码器,对模型轻量化处理,使用深度可分离卷积代替传统的卷积以减少计算成本,提升检测速度。最后,利用TensorRT加速推理,生成Trt模型,方便其部署在ARM嵌入式平台中实现实时车道线检测。在自行制作的Tusimeple扩充数据集上的实验结果表明,所提出的算法能够应对各种复杂交通场景,检测精度达到98.03%,优于其他算法,并且其检测速度达到了50 FSP,满足实时性检测要求。本算法在复杂交通场景下具有较高的鲁棒性和有较好的实时性,具有一定的理论价值和实际应用价值。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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]