Traffic Sign Recognition Through Voice Assistance Using Convolutional Neural Network

Abstract: For self-driving cars and intelligent transportation systems, detecting and recognizing traffic signs is crucial. Real-time traffic sign detection and recognition from camera photos is the task at hand. Across a range of computer vision tasks, Convolutional Neural Networks (CNN) have demonstrated efficacy in achieving high accuracy. In this work, we provide a CNN-based method for identifying and detecting traffic signs. Our method makes use of a deep CNN architecture that is capable of simultaneous traffic sign detection and classification. We use a sizable dataset of photos of traffic signs to train the CNN model, and we assess its effectiveness using a dataset from real-world data. Our test findings show that the suggested method can identify traffic signs in real time with minimal processing overhead and high accuracy.

Keywords: Traffic sign detection, Traffic sign recognition, Deep Learning, Convolutional Neural Network (CNN).