TRAFFIC SIGN RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS (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 roadsigns to provide security and adherence to traffic rules. Machine learning,traditionally applied in TSR, has been extensively applied, but ongoing developmentsin deep learning, with a focus on Convolutional Neural Networks (CNNs), havegreatly enhanced accuracy and durability. CNNs are capable of learning hierarchicalfeature representations automatically, decreasing the reliance on manually craftedfeatures. In this paper, we investigate the use of CNNs for traffic sign recognitionwith a focus on model architectures, training strategies, and performancemeasurement. We introduce a CNN-based TSR model that is optimized for real-timeexecution with low computational cost. Our experiments show that our modelprovides higher accuracy than traditional approaches. Several datasets, such as theGerman Traffic Sign Recognition Benchmark (GTSRB), are utilized for validation.
Keywords: TSR, ITS, CNN, GTSRB