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Automatic Traffic Sign Recognition System Using CNN

Automatic Traffic Sign Recognition System Using CNN

Amritha Barade, Haritha Poornachandran, K. M. Harshitha, Shiloah Elizabeth D., Sunil Retmin Raj C.
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 14
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.300340
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MLA

Barade, Amritha, et al. "Automatic Traffic Sign Recognition System Using CNN." IJIRR vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJIRR.300340

APA

Barade, A., Poornachandran, H., Harshitha, K. M., Shiloah Elizabeth D., & Sunil Retmin Raj C. (2022). Automatic Traffic Sign Recognition System Using CNN. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-14. http://doi.org/10.4018/IJIRR.300340

Chicago

Barade, Amritha, et al. "Automatic Traffic Sign Recognition System Using CNN," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-14. http://doi.org/10.4018/IJIRR.300340

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Abstract

In recent times, self-driving vehicles have been widely adopted across different countries as they are equipped to drastically reduce the number of road accidents and congestion on the road thereby improving the traffic efficiency. To detect, identify, and label the traffic signs on the road in order to help the Advanced Driver Assistance Systems (ADAS) in these autonomous vehicles with navigation details, a Traffic Sign Recognition (TSR) System using a deep convolutional neural network model, Mask RCNN (Mask Regional Convolutional Neural Network), is proposed in this paper that aims to help the autonomous vehicles comprehend the road ahead and safely navigate to the desired destination. This paper presents the detection and labelling of Indian and European Signs and also the results of the system working efficiently under various challenging visibility conditions. The results obtained show that the Mask RCNN model has recorded higher performance compared to all the other CNN models that have been previously used for traffic sign recognition.