• Akshay Dattu Shriram
• Adesh Bhanudas Shelke
• Sanket sukhdev Jedgule
• sobodh ranjit jogdand
• prof.Twinkle Shukla

Traffic sign recognition and detection is a crucial task in autonomous driving and intelligent transportation systems. Deep learning techniques, such as convolutional neural networks (CNNs), have shown remarkable success in detecting and recognizing traffic signs from images. In this paper, we present an overview of recent advances in deep learning-based traffic sign recognition and detection. We discuss the challenges associated with this task and the various techniques used to overcome them. We also analyze the different datasets used for training and evaluation of traffic sign recognition models. Furthermore, we compare and contrast the performance of different deep learning architectures for traffic sign recognition and detection. Finally, we discuss future research directions in this field and the potential applications of traffic sign recognition and detection in real-world scenarios.

"Traffic sign detection and recognition using deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.k208-k216, May-2023, Available :http://www.jetir.org/papers/JETIR2305A26.pdf

"Traffic sign detection and recognition using deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppk208-k216, May-2023, Available at : http://www.jetir.org/papers/JETIR2305A26.pdf