Abstract
Autonomous vehicle design is the present-day research field in which traffic sign recognition and classification is the challenging task. The motive of this research work is to minimize the involvement of human consciousness. Automatic traffic sign recognition systems are designed so that vehicles are capable of choosing right direction, speed, etc. This research paper focuses on the sign classification function. Here we proposed a deep learning approach to design a model to classify road traffic signs. Convolutional neural network (CNN) architecture is designed for the German Traffic Sign Recognition Benchmark (GTSRB) dataset which passes through four layers of convolution operation with increasing filter size and two fully connected layers. Data augmentation and batch normalization techniques are the highlights of this research. Dropout is used to prevent over-fitting and this model achieved 98.65% accuracy in predicting the exact class of unseen images.
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Palak, Sangal, A.L. (2022). Deep Learning Approach to Classify Road Traffic Sign Images. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_14
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DOI: https://doi.org/10.1007/978-3-030-84760-9_14
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