E3S Web Conf.
Volume 412, 2023International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
|Number of page(s)||9|
|Published online||17 August 2023|
Comparative Analysis of Transfer Learning-Based CNN Approaches for Recognition of Traffic Signs in Autonomous Vehicles
Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Traffic signs recognition has a crucial role in enhancing the safety and efficienty of autonomous vehicles (AVs). This AVs can contribute to a cleaner and healthier environment by improving fuel efficiency, minimizing travel distances, and deacreasing air pollution. Many artificial intelligence (AI) approaches contribute to develop AVs. Therfore, Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification tasks for AVs, inculding traffic signs recognition. However, training deep CNNs from scratch for traffic sign recognition requires a significant amount of labeled data, which can be time-consuming and ressource-intensive to obtain. Transfer Learning, a technique that leverages pre-trained models on large-scale datasets,offers a promising solution by enabling the transfer of learned feautres from one task to another. This paper presents a comprehensive comparative analysis of three popular transfer learning based CNN approaches, namely ResNet, VGGNet, and MobileNet,for the recognition of traffic signs in the context of AVs.
Key words: Traffic signs recognition / Convolutional Neural Network / Transfer Learning / Environment / ResNet / VGGNet / MobileNet / Data augmentation / Autonomous Vehicles
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.