MATLAB Code Implementation for Camera-Based Traffic Sign Detection and Recognition

This system utilizes camera input to perform real-time image processing for traffic sign detection and recognition. The implementation involves analyzing and processing images through computer vision algorithms such as edge detection using functions like edge() with Canny or Sobel operators, and color recognition through color space conversion (RGB to HSV) and thresholding operations. Additionally, the system incorporates machine learning and deep learning techniques using MATLAB's Computer Vision Toolbox and Deep Learning Toolbox to train models - potentially employing convolutional neural networks (CNN) with architectures like AlexNet or ResNet - to enhance the accuracy and robustness of traffic sign detection and recognition. The implementation may include using trainNetwork() for model training and classify() for inference. Furthermore, detected traffic sign information can be correlated with map data and other relevant information through data fusion techniques, enabling richer traffic information analysis and applications. This approach facilitates the development of more efficient, accurate, and intelligent traffic sign detection and recognition systems, potentially integrating functions like vision.CascadeObjectDetector for initial detection and image processing pipelines for real-time performance optimization.