Traffic Sign Classification Using Deep Learning Technique

Traffic Sign Classification is a new technology from which a vehicle can recognize the traffic signs that are present in the road. Recognizing these indicators is useful for a variety of applications, such as safety and driverless vehicles. Self-driving vehicles have the potential to create a space that is safe, efficient, and free of crowds. The flawless recognition of traffic signs, lane lines, pedestrians, and other obstacles are just a few of the issues this AI-implemented car autonomy has. However, these issues can be resolved with the use of deep learning techniques. Lately, deep learning strategies have proven to be very successful at identifying traffic signs and have shown that they can represent information. In this work, created a model for effectively recognizing the traffic signs using Convolutional Neural Network (CNN), from the concept of deep learning. The evaluation of the proposed strategy is performed on standard benchmark datasets, such as German Traffic Sign Recognition Benchmark (GTSRB) and Belgian Traffic Sign Dataset, using common metrics like accuracy, precision. The results demonstrate the superiority of deep learning techniques over traditional methods, achieving high accuracy rates and robustness in real-world scenarios.