The Improved Deeplabv3plus Based Fast Lane Detection Method

Lane detection is one of the most basic and essential tasks for autonomous vehicles. Therefore, the fast and accurate recognition of lanes has become a hot topic in industry and academia. Deep learning based on a neural network is also a common method for lane detection. However, due to the huge com...

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Bibliographic Details
Published in:Actuators 2022-07, Vol.11 (7), p.197
Main Authors: Wang, Zhong, Zhao, Yin, Tian, Yang, Zhang, Yahui, Gao, Landa
Format: Article
Language:English
Subjects:
Accuracy
attention distillation
attentional mechanisms
Deep learning
deeplabv3plus
Design
Distillation
lane detection
Methods
Neural networks
Real time
Semantics
Vision systems
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Summary:Lane detection is one of the most basic and essential tasks for autonomous vehicles. Therefore, the fast and accurate recognition of lanes has become a hot topic in industry and academia. Deep learning based on a neural network is also a common method for lane detection. However, due to the huge computational burden of the neural network, its real-time performance is often difficult to meet the requirements in the fast-changing actual driving scenes. A lightweight network combining the Squeeze-and-Excitation block and the Self-Attention Distillation module is proposed in this paper, which is based on the existing deeplabv3plus network and specifically improves its real-time performance. After experimental verification, the proposed network achieved 97.49% accuracy and 60.0% MIOU at a run time of 8.7 ms, so the network structure achieves a good trade-off between real-time performance and accuracy.
ISSN:2076-0825
2076-0825
DOI:10.3390/act11070197