Development of the modified methods to train a neural network to solve the task on recognition of road users

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.164789

Keywords:

pattern recognition, genetic algorithm, evolutionary algorithm, neural networks, Python, OpenCV, Keras

Abstract

We have developed modifications of a simple genetic algorithm for pattern recognition. In the proposed modification Alpha-Beta, at the stage of selection of individuals to the new population the individuals are ranked in terms of fitness, then the number of pairs is randomly determined ‒ a certain number of the fittest individuals, and the same number of the least adapted. The fittest individuals form the subset B, those least adapted ‒ the subset W. Both subsets are included in a set of pairs V. The number of individuals that can be selected to pairs is in the range of 20‒60 % of the total number of individuals. In the modification Alpha Beta fixed compared to the original version of a simple genetic algorithm we added a possibility of the emergence of two mutations, added a fixed point of intersection, as well as changed the selection of individuals for crossbreeding. This makes it possible to increase the indicator of accuracy in comparison with the basic version of a simple genetic algorithm. In the modification Fixed a fixed point of intersection was established. The cross-breeding involves half the genes ‒ those genes that are responsible for the number of neurons in layers, values for other genes are always passed to the descendants from one of the individuals. In addition, at the stage of mutation there are randomly occurring mutations using a Monte-Carlo method.

The developed methods were implemented in software to solve the task on recognizing motorists (cars, bicycles, pedestrians, motorcycles, trucks). We also compared indicators for using modifications of a simple genetic algorithm and determined the best approach to solving the task on recognizing road traffic participants. It was found that the developed modification Alpha-Beta showed better results compared to other modifications when solving the task on recognizing road traffic participants. When applying the developed modifications, the following indicators for the accuracy of Alpha-Beta were obtained ‒ 96.90 %, Alpha‒Beta fixed ‒ 95.89 %, fixed ‒ 85.48 %. In addition, applying the developed modifications reduces the time for the neuromodel’s parameters selection, specifically using the Alpha-Beta modification employs only 73.9 % of the time required by the basic method, applying the Fixed modification ‒ 91.1 % of the time required by the basic genetic method

Author Biographies

Ievgen Fedorchenko, Zaporizhzhya National Technical University Zhukovskoho str., 64, Zaporizhzhya, Ukraine, 69063

Senior Lecturer

Department of Software Tools

Andrii Oliinyk, Zaporizhzhya National Technical University Zhukovskoho str., 64, Zaporizhzhya, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Alexander Stepanenko, Zaporizhzhya National Technical University Zhukovskoho str., 64, Zaporizhzhya, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Tetiana Zaiko, Zaporizhzhya National Technical University Zhukovskoho str., 64, Zaporizhzhya, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Serhii Shylo, Zaporizhzhya National Technical University Zhukovskoho str., 64, Zaporizhzhya, Ukraine, 69063

Senior Lecturer

Department of Electrical and Electronic Apparatus

Anton Svyrydenko

Software Developer

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Published

2019-04-24

How to Cite

Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Shylo, S., & Svyrydenko, A. (2019). Development of the modified methods to train a neural network to solve the task on recognition of road users. Eastern-European Journal of Enterprise Technologies, 2(9 (98), 46–55. https://doi.org/10.15587/1729-4061.2019.164789

Issue

Vol. 2 No. 9 (98) (2019): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2019 Ievgen Fedorchenko, Andrii Oliinyk, Alexander Stepanenko, Tetiana Zaiko, Serhii Shylo, Anton Svyrydenko

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