Traffic Sign Recognition for Automatic Vehicles

In the realm of autonomous driving, traffic sign recognition (TSR) is of utmost importance since it is crucial to the ability of driverless cars to read and understand both permanent and roadside temporary signs that are displayed. This study's primary objective is to develop a comprehensive convolution neural network (CNN) and Densenet201 traffic sign recognition system. The successful implementation of such a system is crucial for the progress of autonomous driving technology, as it significantly contributes to enhancing road safety. We intend to precisely recognize and classify traffic signs by utilizing the strength of a pre-trained Densenet201 model, famous for delivering cutting-edge performance in image recognition applications. We used data augmentation approaches to strengthen the model's robustness and enhance its generalization skills while optimizing it using a large dataset comprising traffic sign photos. Python and the Tensorflow deep learning framework serves as the backbone of our proposed system. By utilizing a publicly available traffic sign dataset, we trained and evaluate our system's performance, and subsequently,compared it against cutting-edge methods. The desired result is a very efficient system for recognizing traffic signs that has a chance to drastically lower road accidents brought on by human error and raise overall road safety.