Lane detection algorithm based on feature correlation
WANG Zhaojing
LIU Biao
LIU Guohao
BIAN Haoyi
Abstract：Addressing the challenges posed by the elongated and easily occluded nature of lane lines in lane de-tection tasks,this study introduces the Cross Convolution Net(C-Net),an instance segmentation network structured on an encoder-decoder architecture,for effective lane detection and recognition.Firstly,a feature as-sociation mechanism based on cross convolution is proposed.Through two consecutive cross-convolution op-erations on the down-sampled feature map,a connection is established between individual feature points and the global features,thereby enlarging the receptive field of the feature map to enhance the network's inferential capabilities.Furthermore,5 dual-channel up-sampling modules are used to up-sample the cross-convolution feature map,yielding the instance segmentation result of lane lines.Finally,the network is trained and com-pared on the Tusimple dataset.The results show that C-Net can achieve an accuracy rate of 96.52%,with low false detection and missed detection rates,highlighting its robust lane detection capabilities.
Keywords：deep learningconvolutional neural networklane detectioncross convolution
Publication Date：2023-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 34-39 )