• Bharatesh Nagaraj Labhagond
• Anirudh Gudisagar
• Sharanabasava
• Rajesh Pawar

Traffic signs are mandatory features of road traffic regulations worldwide. Automatic detection and recognition of traffic signs by vehicles may increase the safety level of drivers and passengers. For this reason, Real Time-Traffic Sign Recognition system is one of the essential components for smart transportation systems and high-tech vehicles. Recently, very good performances have been achieved in public datasets, especially with advanced Computer Vision (CV) approaches like Deep Learning or more precisely CNN (neural networks) due to high recognition rate and fast execution.CNN has largely influenced all the computer visionary tasks. So, in this project we propose a deep network traffic sign recognition/classification model with the help of python as the base language and followed by different python libraries for training the CNN model. This model will consist of different CNN layers which will precisely classify interclass samples from the dataset which will be provided. This system will be more efficient for recognizing the real time traffic sign and also tell from which class a particular sign belongs with name. Therefore, this study makes TSR software has been developed by using Convolutional Neural Networks (CNN) built on DL techniques along with CV techniques. Coding is accomplished under TensorFlow and OpenCV frameworks with the python programming language and CNN training is carried out by using parallel architecture. The experimental findings indicate that the developed CNN architecture achieves greater accuracy and confirms the high efficiency of the system.

Deep Learning, Convolutional Neural Networks, traffic signs

"Traffic Signs Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.m508-m514, April-2024, Available :http://www.jetir.org/papers/JETIR2404C70.pdf

"Traffic Signs Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppm508-m514, April-2024, Available at : http://www.jetir.org/papers/JETIR2404C70.pdf