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http://dx.doi.org/10.12815/kits.2020.19.5.178

A Study on Lane Detection Based on Split-Attention Backbone Network  

Song, In seo (Dept. of Electrical and Computer Eng., Inha Univ.)
Lee, Seon woo (Dept. of Electrical and Computer Eng., Inha Univ.)
Kwon, Jang woo (Dept. of Computer Eng., Inha Univ.)
Won, Jong hoon (Dept. of Electronic Eng., Inha Univ.)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.5, 2020 , pp. 178-188 More about this Journal
Abstract
This paper proposes a lane recognition CNN network using split-attention network as a backbone to extract feature. Split-attention is a method of assigning weight to each channel of a feature map in the CNN feature extraction process; it can reliably extract the features of an image during the rapidly changing driving environment of a vehicle. The proposed deep neural networks in this paper were trained and evaluated using the Tusimple data set. The change in performance according to the number of layers of the backbone network was compared and analyzed. A result comparable to the latest research was obtained with an accuracy of up to 96.26, and FN showed the best result. Therefore, even in the driving environment of an actual vehicle, stable lane recognition is possible without misrecognition using the model proposed in this study.
Keywords
Lane detection; Autonomous driving; Convolutional neural network; AI; Deep learning;
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