In this project a deep learning approach is
presented for detecting traffic signs in real-time for
autonomous vehicle applications. The suggested method
analyzes photos and detects traffic signs using a
convolution neural network (CNN). Using real-world
datasets, the performance of the suggested solution was
assessed and contrasted with currently available state-of-
the-art techniques. According to the outcomes, the
suggested method performs better than earlier ones in
terms of accuracy and processing speed, making it a
viable option for autonomous vehicles' traffic sign
identification. This approach has the potential to
significantly improve the safety and efficiency of
autonomous vehicle navigation and facilitate the
widespread adoption of autonomous vehicles. Traffic
sign detection is a crucial aspect of autonomousvehicle
technology as it helps vehicles to understand and
respond to the road conditions and traffic regulations.
Keywords : Deep Learning, Image Processing, Anaconda, CNN.