OBJECT RECOGNITION SYSTEM FOR COMPONENT AUTOMATION USING A CONVERGULAR NEURAL NETWORK
DOI:
https://doi.org/10.26906/SUNZ.2023.2.068Keywords:
neural network, machine learning, object recognition, CPU, GPUAbstract
Topicality. With the increase in online sales in the world, the need for warehouse automation systems is growing. With a large number of products, the question arises of recognizing products that are similar in appearance, but have different characteristics. In this regard, the use of elements of artificial intelligence and the construction of computer vision systems for large warehouse enterprises is a necessity. The purpose of the work is to build a system for recognizing various goods using convolutional neural networks. The object of the study is the process of building and training a system for recognizing goods in a warehouse. The subject of the research is object recognition methods based on a neural network using CPU and GPU. Conclusion. An object recognition system was built based on convolutional neural networks in the MatLab environment. Experiments were conducted using CPU and GPU for neural network training. The obtained results showed that to increase the quality of recognition, it is necessary to increase the test sample.Downloads
References
Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, Int. Journal of Advanced Trends in Computer Science and Engineering, 8(1), pp. 66-69, DOI: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019
Коваленко А. А., Кучук Г. А. Методи синтезу інформаційної та технічної структур системи управління об’єктом критичного застосування. Сучасні інформаційні системи. 2018. Т. 2, № 1. С. 22–27. DOI: https://doi.org/10.20998/2522-9052.2018.1.04
Свиридов А. C., Коваленко А. А., Кучук Г. А. Метод перерозподілу пропускної здатності критичної ділянки мережі на основі удосконалення ON/OFF-моделі трафіку. Сучасні інформаційні системи. 2018. Т. 2, № 2. С. 139–144. DOI: https://doi.org/10.20998/2522-9052.2018.2.24
R. Bekkerman, M. Bilenko, J. Langford, Scaling Up Machine Learning, Cambridge University Press, January 2012
Mitchell, T. (1997). Machine Learning. McGraw Hill.
Goodfellow, Ian, Yoshua Begnio, and Aaron Courville. 2016. Deep Learning. Cambridge:MIT Press
Krizhevsky, A., Sutskever, I., Hinton, G. E. (2017), "ImageNet classification with deep convolutional neural networks", Communications of the ACM, Vol. 60, No. 6
Alexander Toshev, Christian Szegedy (2014), “DeepPose: Human Pose Estimation via Deep Neural Networks”, DOI: https://doi.org/10.1109/CVPR.2014.214
I. Mykhailichenko, H. Ivashchenko, O. Barkovska and O. Liashenko, "Application of Deep Neural Network for Real-Time Voice Command Recognition," 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 2022, pp. 1-4, doi: 10.1109/KhPIWeek57572.2022.9916473.
