Efficient Traffic Sign Detection Using CNNs: A Scalable Deep Learning Model

Traffic sign detection plays a crucial role in intelligent transportation systems, enhancing road safety and efficiency. Convolutional Neural Networks (CNNs) have shown remarkable performance in this domain. However, achieving optimal results requires careful preprocessing of the input data. In this study, we investigate the impact of preprocessing techniques, including image resizing, normalization, data augmentation, grayscale conversion, and histogram equalization, on the performance of various deep learning models for traffic sign detection. Image resizing is employed to standardize the dimensions of input images, facilitating model training and inference. Normalization is applied to scale pixel values, reducing the influence of lighting and enhancing model robustness. Data augmentation techniques such as rotation, translation, and flipping are utilized to increase the diversity of the training dataset, thereby improving model generalization. Grayscale conversion is explored to reduce computational complexity and enhance model efficiency, while histogram equalization aids in improving contrast and visibility of traffic signs. The effectiveness of these preprocessing techniques is evaluated across a range of popular deep learning architectures, including CNN, ResNet, LeNet, DenseNet, and Inception. Each model's performance is assessed based on metrics such as accuracy, precision, recall, and F1-score. Comparative analysis reveals the impact of preprocessing on model performance and highlights the strengths and weaknesses of different architectures in traffic sign detection tasks. This study contributes to the understanding of preprocessing techniques' role in optimizing deep learning models for traffic sign detection, providing insights for researchers and practitioners working in the field of computer vision and intelligent transportation systems.