Abstract
Object recognition is a large application area of Machine Learning (ML) aiming to design solutions to autonomous driving capable to accurately identify traffic signs. Such applications have to minimise risks of making incorrect decisions and must have the highest recognition accuracy. In this paper we explore and compare ML methods such as Artificial Neural Networks and Convolutional Neural Networks on German Traffic Sign Recognition Benchmark. Based on the experiments we conclude that ML methods require massive experiments in order to maximise the performance and particularly in our case of study to achieve almost 100% recognition accuracy.
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Cox, D., Biel, A., Hoque, F. (2022). Experimental Design of Artificial Neural-Network Solutions for Traffic Sign Recognition. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_23
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DOI: https://doi.org/10.1007/978-3-030-82199-9_23
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