TRAFFIC SIGNAL DETECTION SYSTEM BY USING DEEP LEARNING (CNN ALGORITHM)

Traffic signal detection plays a crucial role in ensuring road safety and efficient traffic management. In this research paper, we propose a method for detecting traffic signals using a Convolutional Neural Network (CNN) algorithm and deep learning techniques. Our approach leverages the ability of CNNs to automatically learn discriminative features from images and generalize well to unseen data. We present a comprehensive study that includes dataset acquisition and preprocessing, CNN architecture selection and justification, training process and parameter optimization, and evaluation of the trained model on a test dataset. The experimental results demonstrate the effectiveness and accuracy of our proposed method in detecting traffic signals, providing a promising solution for real-world applications. Deep learning-based approaches, on the other hand, have shown promising results in traffic signal detection. These approaches involve training a neural network model on a large dataset of labeled images of traffic signals. The model can then be used to detect traffic signals in new images. Object detection models such as Faster R-CNN, YOLO( v5 ), and SSD are commonly used for traffic signal detection.