OKORO SUNDAY OSAMWONYI

This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and
generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based
image recognition for applications in autonomous driving and traffic management. This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational
constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based image recognition for applications in autonomous driving and traffic management.

This project implements a deep learning algorithm for image recognition, focusing ontrafficsign classification. Traditional machine learning methods struggle with manual featureextraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracyandgeneralization. Using TensorFlow and Keras, experiments were conducted on the German TrafficSignRecognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfittingledto60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths inGPUacceleration and modular architecture. Challenges like overfitting and computational constraints persist. Future research should explore transfer learning, ensemble methods, andreal-time optimization to enhance performance. This study advances deep learning-basedimage recognition for applications in autonomous driving and traffic management. This project implements a deep learning algorithm for image recognition, focusing ontrafficsign classification. Traditional machine learning methods struggle with manual featureextraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracyandgeneralization. Using TensorFlow and Keras, experiments were conducted on the German TrafficSignRecognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfittingledto60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths inGPUacceleration and modular architecture. Challenges like overfitting and computational constraints persist. Future research should explore transfer learning, ensemble methods, andreal-time optimization to enhance performance. This study advances deep learning-basedimage recognition for applications in autonomous driving and traffic management.