Abstract
Traffic sign classification is an important aspect of autonomous driving systems. A slight improvement on classification performance can potentially lower the rate of car accidents. In view of this, we propose three different deep convolutional neural networks in a hierarchical pattern, yet not convoluted among themselves for classifying traffic sign. A very popular and reliable traffic sign dataset called GTSRB is used to train our proposed networks. In our work, we present a novel approach to classify images. Furthermore, we modify all three convolutional neural networks over some of the existing neural nets. While modifying the networks, we redesign them based on specific requirements which may also prove handy for other datasets. Along with the new methods, we are able to reduce the computational complexity as well. On top of the new architecture, we achieve a notably higher accuracy in performance of 99.92% surpassing the state-of-the-art performance of 99.81%. In a nutshell, we trained an artificial intelligence (AI) model that learns to chose between two different AI models while classifying an image.
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Saha, S., Islam, M.S., Khaled, M.A.B., Tairin, S. (2019). An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_74
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DOI: https://doi.org/10.1007/978-981-13-1501-5_74
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