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 |Pages: 14
DOI: 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.
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Background

With the advent of self-driving cars, there have been numerous approaches to identify traffic signs using machine learning techniques. To facilitate this work, numerous datasets are available from around the world. These datasets may consist of videos, images, or just traffic sign images. This field of research is very wide, with numerous approaches to consider. While applications may vary, the main idea that such a system should operate in real time is common to most research papers. The evolution of machine learning has brought this goal closer to reality with newer algorithms employed every year to achieve better accuracy and throughput rate.

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