Abstract
In recent years, taking into account the development of the needs of human society, a great need has arisen for the automation of various processes. To solve such problems, are used neural network technologies, which are attempts to reproduce the human nervous system, are actively gaining great popularity. In particular, neural networks are actively used in the tasks of classifying objects on images, and therefore, based on this, the task of developing new or modifying existing algorithms is relevant.
This article presents an analysis of existing algorithms for using neural systems to classify objects in images, as well as technologies for constructing machine vision systems. An attempt is made to develop the architecture of a system for detecting objects on images, is capable of scaling both vertically and horizontally, which is characterized by sufficient accuracy and speed.
Based on the developed architecture, a system for automating the process of classifying objects in images was developed. To give recommendations, a software module was developed that uses machine learning to identify the rules by which the class of the image object should be made. Neural models implementing the classification of objects in images were also configured and trained. The classification results are checked on a real data set, which is similar in composition of attributes to that which will be during production use of the system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldi, P., Hornik, K.: Neural networks and principal component analysis: learning from examples without local minima. Neural Netw. 2(1), 53–58 (1989). http://dx.doi.org/10.1016/0893-6080(89)90014-2
Cho, Y., Saul, L.K.: Kernel methods for deep learning. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems. NIPS 2009, pp. 342–350. Curran Associates Inc., USA (2009). http://dl.acm.org/citation.cfm?id=2984093.2984132
Ramachandran, P., Liu, P.J., Le, Q.V.: Unsupervised pretraining for sequence to sequence learning. CoRR (2016). http://arxiv.org/abs/1611.02683
Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Jia, Y., Szegedy, C., Liu, W., Sermanet, P., Reed, S.E., et al.: Going deeper with convolutions. CoRR (2014). http://arxiv.org/abs/1409.4842
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR (2013). http://arxiv.org/abs/1311.2524
Apacible, J., Chilimbi, T., Suzue, Y.: Project adam: building an efficient and scalable deep learning training system. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2014), pp. 571–582. USENIX Association, Broomfield (2014). https://www.usenix.org/conference/osdi14/technical-sessions/presentation/chilimbi
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. CoRR (2015). http://arxiv.org/abs/1506.02640
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real - time object detection with region proposal networks. CoRR (2015). http://arxiv.org/abs/1506.01497
Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with CUDA. Queue 6(2), 40–53 (2008). http://doi.acm.org/10.1145/1365490.1365500
Grinberg, M.: Flask Web Development: Developing Web Applications with Python, 1st edn. O’Reilly Media, Inc. (2014)
Da Silva, M.D., Tavares, H.L.: Redis Essentials. Packt Publishing (2015)
Sigismondo, G., Boschi, S.: RabbitMQ Cookbook. Packt Publishing (2013)
Acknowledgments
The reported study was funded by RFBR, project number 19-31-90070.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Smagin, S., Manzhula, I. (2020). Development of the Architecture of the System for Detecting Objects in Images. In: Silhavy, R. (eds) Intelligent Algorithms in Software Engineering. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-51965-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-51965-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51964-3
Online ISBN: 978-3-030-51965-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)