Traffic Sign Recognition and Classification with Convolution Neural Network (CNN) and OpenCV

Traffic Sign Detection Using Convolutional Neural Networks (CNN) and OpenCV presents an innovative approach to enhance road safety through computer vision technology. This study conducts a comparative analysis of traffic sign detection methodologies by evaluating the effectiveness of Convolutional Neural Networks (CNN) and OpenCV compared to existing techniques. The existing system, which typically employs traditional computer vision methods, Haar Cascade classifiers, and template matching, serves as a baseline for comparison. The proposed approach integrates CNN for feature learning and OpenCV for preprocessing, leveraging the strengths of both technologies. A diverse dataset encompassing various traffic sign types and environmental conditions is used for training and testing. Results demonstrate that the CNN and OpenCV combination outperforms traditional methods in terms of accuracy, especially in challenging scenarios such as variable lighting and occlusions. This project sheds light on the advancements brought by deep learning techniques, showcasing the potential of CNN and OpenCV integration for robust and real-time traffic sign detection, crucial for developing intelligent transportation systems and enhanced road safety.