Classification of Traffic Signs with Convolutional Neural Network

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  • Halit Çetiner Isparta University of Applied Sciences


Traffic Sign, Driver Safety, Convolutional Neural Network, Preprocessing, Computer Vision


Rapidly changing and developing technology and related investments are growing rapidly. Despite the rapid development of technology, there are problems that continue as a problem. It is necessary to deal with the problems that are still seen as a problem today by taking advantage of the possibilities and opportunities of technology. Traffic signs should be distinguished by drivers both at night and during the day and should be easily perceived in terms of life safety. At the same time, artificial intelligence supported solutions should be produced in order to reduce vehicle accidents caused by human errors. In order to achieve this, a CNN-based deep learning model suitable for real-time work has been proposed. The running performance of the proposed deep learning model was measured according to the raw input and preprocessed image type. According to the KFold 3 technique, training and test data were separated and the proposed model was trained. As a result of the experimental studies, 99% precision, recall, F1 score, and accuracy measurement metrics were achieved with the CNN model with preprocessed input type. According to the raw input type that has not undergone any preprocessing, success rates of 98%, 97%, 98%, and 98% were achieved in terms of precision, recall, F1 score, and accuracy metrics, respectively.

Author Biography

Halit Çetiner, Isparta University of Applied Sciences

Vocational School of Technical Sciences, Turkey


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How to Cite

Çetiner, H. (2023). Classification of Traffic Signs with Convolutional Neural Network. International Conference on Frontiers in Academic Research, 1, 83–94. Retrieved from