Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems

Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021, Vol.51 (1), p.124-142
Main Authors: Bayoudh, Khaled, Hamdaoui, Fayçal, Mtibaa, Abdellatif
Format: Article
Language:English
Subjects:
Advanced driver assistance systems
Algorithms
Artificial Intelligence
Computer Science
Computer vision
Deep learning
Domains
Drivers
Intelligent transportation systems
Machine learning
Machines
Manufacturing
Mechanical Engineering
Object recognition
Processes
Semantics
System effectiveness
Three dimensional models
Traffic congestion
Traffic control
Traffic signs
Two dimensional models
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Summary:Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01801-5