Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 416-419.doi: 10.11896/jsjkx.201100206

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Lightweight Lane Detection Model Based on Row-column Decoupled Sampling

CHEN Hao-nan, LEI Yin-jie, WANG Hao   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:CHEN Hao-nan,born in 1996,postgra-duate.His main research interests include deep learning and computer vision.
    LEI Yin-jie,born in 1983,Ph.D,asso-ciate professor,Ph.D supervisor.His main research interests include deep learning and computer vision.
  • Supported by:
    National Natural Science Foundation of China(61972435).

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Abstract: With the development of deep learning,lane detection model based on deepconvolution neural network has been widely applied in autonomous driving system and advanced driving assistant system.These models achieve high accuracy but usually have the disadvantages of large computation and high latency.In order to solve this problem,a specially designed lightweight network for lane detection is proposed.Firstly,a convolution module with row-column decoupled sampling is proposed,which optimizes traditional residual convolution module by utilizing the row-column decomposability of lane area in the image.Secondly,the depth-wise separable convolution technology is used to further reduce the computational complexity of the row-column decoupled sampling convolution module.In addition,a pyramid dilation convolution module is designed to increase the receptive field of the mo-del.The experimental results on CULane dataset show that comparing with the state of the art model SCNN,the floating-point ope-rations of our model is reduced by 95.2% and F1-score is increased by 1.0%.The computation cost of lane detection model is significantly reduced while maintaining high accuracy.

Key words: Computer vision, Convolution neural network, Lane detection, Lane segmentation, Lightweight model

CLC Number: 

  • TP391
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