• Keerthan S
• Dr. Shashidhar Kini K

This study focuses on the development of an intelligent traffic sign detection system using deep learning techniques. The system is designed to assist autonomous driving and advanced driver-assistance systems (ADAS) by accurately identifying traffic signs from real-time video feeds or images. Using convolutional neural networks (CNNs), the model is trained on annotated datasets containing a wide variety of traffic signs. The study involves data preprocessing, augmentation, CNN architecture design, training with transfer learning, and evaluation. Experiments show promising accuracy, making it suitable for real-time deployment in smart vehicle systems.

Traffic Sign Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Object Detection, YOLO, Computer Vision, Autonomous Driving, Road Safety, Dataset Augmentation.

"TRAFFIC SIGN DETECTION WITH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1096-1099, July-2025, Available :http://www.jetir.org/papers/JETIRGX06208.pdf

"TRAFFIC SIGN DETECTION WITH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1096-1099, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06208.pdf