in: 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering, D. Jude Hemanth,Tuncay Yigit,Utku Kose,Ugur Guvenc, Editor, Springer Nature, Zug, pp.197-217, 2023
Considering the existence of autonomous vehicles, it is seen that many
studies have been done on the traffic light classification recently. Automatic determination of traffic lights can significantly prevent traffic accidents. As the number
of vehicles on the road increases daily, such a classification process becomes crucial. The classification process appears to result in higher accuracy using deep
learning approaches. In this study, a deep learning-based classification process is
performed for traffic lights. A convolutional neural network model with efficient
parameters is proposed. Additively, hyperparameter adjustment is made. In addition to this, the effects of color spaces and input image sizes on the classification
results are investigated. There are four classes of images with red, yellow, green,
and off tags in the database used. When the results are examined, it is seen that
the classification accuracy of over 96% is achieved.