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).

Cite:
B. Haritha, T. Venkata Sai Bhargavi, Y. Hemasri, N. Venkata Amrutha,"Traffic Sign Recognition Through Voice Assistance Using Convolutional Neural Network", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11341.

| DOI: 10.17148/IARJSET.2024.11341