Traffic Sign Recognition: Techniques, Applications and Future Directions

Traffic sign recognition (TSR) is a crucial component of intelligent transportation systems, enhancing road safety and automating vehicle navigation. This project aims to develop a robust TSR system leveraging advanced machine learning and computer vision techniques. By utilizing a convolutional neural network (CNN) architecture, we analyse images of traffic signs to classify them accurately. The dataset consists of diverse images sourced from real-world driving environments, ensuring the model's adaptability to various lighting and weather conditions. Our methodology includes data augmentation and transfer learning to improve model performance. The results demonstrate a significant increase in accuracy compared to traditional methods, achieving over 95% classification accuracy on test datasets. This system not only aids in driver assistance technologies but also contributes to the development of autonomous vehicles, ultimately promoting safer roadways. Future work will focus on real-time implementation and further enhancements to handle occluded and partially visible signs.