An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems

Authors

  • Akshay Utane, Sharad Mohod, Ashay Rokade, Yogesh Thakare, Hemant Kasturiwale

Keywords:

Advanced Driving Assistance Systems (ADAS), Convolutional Neural Network (CNN), Deep Learning, Ensemble Learning, Machine Learning, Traffic Sign Recognition.

Abstract

The research paper introduces an automatic traffic sign identification system tailored for the distinctive challenges posed by Indian traffic scenarios. This system leverages deep learning for feature extraction and ensemble learning for classification, effectively sorting traffic signs into their fundamental categories. The paper underscores the crucial significance of precise traffic sign recognition in fortifying autonomous driving assistance systems (ADAS) and ensuring the secure flow of vehicles on roads. Through extensive evaluation using Indian traffic sign databases, the proposed system exhibits superior performance when compared to existing technologies, significantly augmenting the overall efficiency of the recognition process. The reported performance analysis of 91.10% underscores the system's effectiveness in addressing the complex requirements of traffic sign recognition, thereby mitigating potential risks to public health, the environment, and infrastructure.

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References

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Published

16.03.2024

How to Cite

Ashay Rokade, Yogesh Thakare, Hemant Kasturiwale, A. U. S. M. . (2024). An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1222–1229. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5402

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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