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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: This research paper investigates the development of deep learning models for traffic sign recognition in autonomous vehicles. Leveraging convolutional neural networks (CNNs), the study explores various architectural configurations and evaluation methodologies to assess the efficacy of CNNs in accurately identifying and classifying traffic signs. Through a systematic evaluation process utilizing metrics such as accuracy, precision, recall, and F-score, the research demonstrates the robustness and generalization capability of the developed models across diverse environmental conditions. Furthermore, the utilization of visualization techniques, including the Matplotlib library, enhances the interpretability of model training dynamics and optimization progress. The findings highlight the significance of CNN architecture in facilitating hierarchical feature extraction and spatial dependency learning, thereby enabling reliable and efficient traffic sign recognition. The successful recognition of traffic signs under varying lighting conditions underscores the resilience of the developed models to environmental perturbations. Overall, this research contributes to advancing the capabilities of autonomous vehicle systems and lays the groundwork for the implementation of intelligent traffic sign recognition systems aimed at enhancing road safety and navigational efficiency.
Zhadra Kozhamkulova, Zhanar Bidakhmet, Marina Vorogushina, Zhuldyz Tashenova, Bella Tussupova, Elmira Nurlybaeva and Dastan Kambarov, “Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150593
@article{Kozhamkulova2024,
title = {Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150593},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150593},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Zhadra Kozhamkulova and Zhanar Bidakhmet and Marina Vorogushina and Zhuldyz Tashenova and Bella Tussupova and Elmira Nurlybaeva and Dastan Kambarov}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.