To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Riadh Ayachi (Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia)
Yahia ElFahem Said (1Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia. 2Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia)
Mohamed Atri (Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia)

Abstract


Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making. The evaluation of the proposed pipeline was trained and tested using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real time application.


Keywords


Traffic signs classification; Autonomous vehicles; Artificial intelligence; Deep learning; Convolutional Neural Networks CNN; Image understanding

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References


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DOI: https://doi.org/10.30564/aia.v1i1.569

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