TRAFFIC SIGNS RECOGNITION USING CNN AND KERAS IN PYTHON

Traffic sign recognition is an important component of self-driving systems because it helps vehicles accurately perceive and respond to traffic signs. We show in this study a system for reading traffic signs that uses Convolutional Neural Networks (CNNs) and the Keras framework in Python. The proposed algorithm uses a collection of traffic signs to recognize and classify them in real time with high accuracy. Our method uses deep learning to accomplish reliable recognition under a variety of situations, including lighting, rotation, and occlusion. The system is examined using the German Traffic Sign Recognition Benchmark (GTSRB), and it performs comparably to cutting-edge approaches. We also go over the model architecture, training procedure, and potential enhancements for use in real-world applications.

 

Keywords: Traffic sign recognition, CNN, Keras, deep learning, autonomous driving, Python, GTSRB, image classification.

Traffic sign recognition CNN Keras deep learning autonomous driving Python GTSRB image classification.