Traffic Sign Recognition using ML and Image Processing

Abstract

TSRS (Traffic Sign Recognition System) may plays a significant role in self driving car, artificial driver assistances, traffic surveillance as well as traffic safety. Traffic sign recognition is necessary to overcome the traffic related difficulties. The traffic sign recognition system has two parts localization and recognition. In localization part, where traffic sign region is located and identified by creating a rectangular area. After that, in recognition part the rectangular box provided the result for which traffic sign is located in that particular region. In this paper, we describe an approach towards traffic signs recognition system

Introduction

I. INTRODUCTION

With the rapid growth of technological development, vehicles have become an essential portion of in our routine lives. Because driving vehicles without follow traffic rules, it creates more and more intricate traffic on the road. As a result, it is one of the major reasons behind accidents every year. In recent times road accidents are happening regularly in increasing manner across the world. Leading reason of most road accidents is the ignorance or unawareness of the traffic sign. The meaning of traffic sign is any entity, device, or board on the road that entity carries the rules, indicates the warning or provides other explanation regarding driving. Therefore, it also provides necessary information through traffic signals and traffic control devices to continue smooth car driving.

II. MODULE IDENTIFICATION

Training takes place after a data set has been created and preprocessed with rotation, resizing, grayscale conversion, and normalization. As the number of epochs is set, themodel will be trained so that as the accuracy grows, the training loss will decrease. Having done this, we have completed the evaluation process.  After the training is complete, we have a Neural Network model. The resultant Neural Network model was used to further recognize traffic signs in live streams. Using OpenCV libraries, we could detect any sign put in front of the camera by simply displaying a green box bound to the sign along with the traffic sign. We tested the model to ensure that it produces accurate results. It is then repeated with new data if necessary.

Not only does the model accurately recognize traffic signs but it also provides traffic sign to text conversion.

III. SCOPE

Traffic-sign recognition is a safety tech system that recognizes traffic signs and relays the information displayed on the sign to the driver through the instrument cluster. The primary purpose of TSR is to increase driver focus.  If a driver misses a sign, TSR can make them aware of it so they can react accordingly.

IV. EXISTING SYSTEM

Conclusion

Traffic sign recognition is a difficult task if aim is at detecting and recognizing signs in images captured from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may make it difficult to detect and recognize signs in the rural as well as the urban areas.In future, our aim is to increase the number of traffic sign classes with large amount of quality data. As in machine learning research, to maintain data volume and data quality.

References

[1] Yang, WJ., Luo, CC., Chung, PC., Yang, JF.: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition. Lecture Notes in Networks and Systems, vol 69. Springer, Cham (2020) [2] Dubey, A.R., Shukla, N., Kumar, D.: Detection and Classification of Road Signs Using HOG-SVM Method. Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, [3] Dhar, M. Z., Abedin, T., Biswas, Datta, A.: Traffic sign detection — A new approach and recognition using convolution neural network. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 416-419, (2017) [4] Aashrith, V., Smriti, S.: Traffic Sign Detection and Recognition using a CNN Ensemble. IEEE International Conference on Consumer Electronics (ICCE). 07 March, (2019) [5] Di, Z., Junqi, Z., Dongdong, Z.: Traffic sign detection based on cascaded convolutional neural networks. In: Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), (2016) [6] Sandy, A., Chih-Jung, C., Hsueh-Ming H.: Real-time traffic sign recognition using color segmentation and SVM. In: Conference on Systems, Signals and Image Processing (IWSSIP), (2017) [7] Jian-He, S., Huei-Yung, L.: A vision system for traffic sign detection and recognition. IEEE 26th In: Symposium on Industrial Electronics (ISIE), (2017) [8] Zaki, P. S., et al.: Traffic Signs Detection and Recognition System using Deep Learning. In: Conference on Intelligent Computing and Information Systems (ICICIS) (2019) [9] Jin, Y., Fu, Y., Wang, W., Guo, J., Ren, C., Xiang, X.: Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition. in IEEE Access, vol. 8, pp. 38931-38940, (2020) [10] Saadna, Y., Behloul, A.: An overview of traffic sign detection and classification methods. In: J Multimed Info Retr 6, 193–210. https://doi.org/10.1007/s13735- 017-0129-8 (2017) [11] Sheikh , M. A. A., Kole , A., Maity, T.: Traffic sign detection and classification using colour feature and neural network. In: Conference on Intelligent Control Power and Instrumentation (ICICPI), Kolkata, pp. 307-311. (2016) .

Copyright

Copyright © 2023 Dr. Ninad More, Lahu Mahadev Kamble, Prajwal Ananda Jadhav, Mahesh Pundalik Patil, Shrikant Gokul Potle . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.