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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4505-4516
S&M2425 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3128
Published: December 29, 2020

Improved Lane Detection Method Based on Convolutional Neural Network Using Self-attention Distillation [PDF]

Xinyu Zhang, He Huang, Weiming Meng, and Dean Luo

(Received September 29, 2020; Accepted December 9, 2020)

Keywords: high-precision map, semantic segmentation, lane detection, DC-VGG-SAD, detection accuracy, detection speed

With the rapid development of autopilot technology and various types of sensor, high-precision maps containing a large amount of information for assisting driving have been proposed. The standard lane line detection algorithm relies on the robust estimation of visible lane line markers from a camera image using vision and image processing algorithms. Although the recognition and detection technology for road marking lines is relatively mature, some problems still exist, such as poor detection accuracy and unsatisfactory real-time performance. To solve the problems of the poor robustness and low running speed of the current lane detection methods in complex environments, in this study, we improve current lane detection methods from the perspective of semantic segmentation and propose a DC-VGG-SAD network (VGG: visual geometry group), in which dilated convolution (DC) is used to reduce the complexity of the network to ensure detection accuracy. Furthermore, adding self-attention distillation (SAD) makes the information transmission faster. The proposed network was experimentally evaluated using two large-scale datasets. It was found that when dealing with lane lines in complex environments, the network offers higher detection accuracy and detection speed than most current mainstream networks.

Corresponding author: He Huang


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Cite this article
Xinyu Zhang, He Huang, Weiming Meng, and Dean Luo, Improved Lane Detection Method Based on Convolutional Neural Network Using Self-attention Distillation, Sens. Mater., Vol. 32, No. 12, 2020, p. 4505-4516.



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