(untitled) (International Journal of Engineering Management Science, 2017)
Traffic sign recognition (TSR) plays a vital part in intelligent transport systems (ITS) and driverless cars by allowing real-time identification and interpretation of road signs to provide security and adherence to traffic rules. Machine learning, traditionally applied in TSR, has been extensively applied, but ongoing developments in deep learning, with a focus on Convolutional Neural Networks (CNNs), have greatly enhanced accuracy and durability. CNNs are capable of learning hierarchical feature representations automatically, decreasing the reliance on manually crafted features. In this paper, we investigate the use of CNNs for traffic sign recognition with a focus on model architectures, training strategies, and performance measurement. We introduce a CNN-based TSR model that is optimized for real-time execution with low computational cost. Our experiments show that our model provides higher accuracy than traditional approaches. Several datasets, such as the German Traffic Sign Recognition Benchmark (GTSRB), are utilized for validation.