Abstract
In this paper we’ve conducted multiple experiments with modern object detection system YOLO. Object detection systems are fundamental to many robotics tasks. Recognition algorithms involving object detection are often part of various intelligence systems for robots. Training object detection systems usually requires waste amounts of training data which can be expensive and time-consuming. In this paper we’ve conducted several experiments with YOLO on small training datasets investigating YOLO’s capacity to train on small number of examples. We measured accuracy metrics for object detector depending on the size of training dataset, compared training process of full and smaller versions of YOLO and their speed. Gathered information will be used for creating visual factographic intelligence system for robots. YOLO (You Only Look Once) is a special intelligent technology for computer vision techniques. Our results are useful for industry professionals and students from a broad range of disciplines related to robotics, intelligent technologies and other fields.
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
Samsonovich, A.V.: On semantic map as a key component in socially-emotional BICA. Biol. Inspired Cogn. Arch. 23, 1–6 (2018). https://doi.org/10.1016/j.bica.2017.12.002
Voznenko, T.I., Chepin, E.V., Urvanov, G.A.: The control system based on extended BCI for a robotic wheelchair. Procedia Comput. Sci. 123, 522–527 (2018)
Nuzhdin, Y.O., Shishkin, S.L., Fedorova, A.A., Trofimov, A.G., Svirin, E.P., Kozyrskiy, B.L., Medyntsev, A.A., Dubynin, I.A., Velichkovsky, B.M.: The expectation based eye-brain-computer interface. an attempt of online test. In: Proceedings of the 2017 ACM Workshop on An Application-oriented Approach to BCI out of the Laboratory. ACM, pp. 39–42 (2017)
Veselov, D.S.: Sensitive elements based on dielectric membrane structures. Phys. Procedia 72, 499–502 (2015)
Kireev, V.S., Guseva, A.I., Bochkaryov, P.V., Kuznetsov, I.A., Filippov, S.A.: Association rules mining for predictive analytics. In: IoT Cloud System. Advances in Intelligent Systems and Computing, vol. 848, pp. 107–112 (2018)
Kolychev, V.D., Shebotinov, A.A.: Application of business intelligence instrumental tools for visualization of key performance indicators of an enterprise in telecommunications. Sci. Vis. 11(1), 20–37 (2019)
Verbitsky, N.S., Chepin, E.V., Gridnev, A.A.: Experimental studies of a convolutional neural network for application in the navigation system of a mobile robot. Procedia Comput. Sci. 145, 611–616 (2018)
Artamonov, A., Onykiy, B., Ananieva, A., Ionkina, K., Kshnyakov, D., Danilova, V., Korotkov, M.: Regular agent technologies for the formation of dynamic profile. Procedia Comput. Sci. 88, 482–486 (2016)
Yasnitsky, L.N., Vauleva, S.V., Safonova, D.N., Cherepanov, F.M.: The use of artificial intelligence methods in the analysis of serial killers’ personal characteristics. Criminol. J. Baikal Natl. Uni. Econ. Law 9(3), 423–430 (2015)
Miloslavskaya, N., Tolstoy, A.: Big data, fast data and data lake concepts. Procedia Comput. Sci. 88, 300–305 (2016)
Samsonovich, A.V.: Emotional biologically inspired cognitive architecture. Biol. Inspired Cogn. Arch. 6, 109–125 (2013). https://doi.org/10.1016/j.bica.2013.07.009
Kulik, S.D.: Factographic information retrieval for semiconductor physics, micro- and nanosystems. In: AMNST 2017, IOP Conference Series: Materials Science and Engineering, vol. 498, p. 012026 (2019)
Kulik, S.: Factographic information retrieval for communication in multicultural society. In: Procedia—Social and Behavioral Sciences (International Conference on Communication in Multicultural Society, CMSC 2015, 6–8 Dec 2015, Moscow, Russian Federation), vol. 236, pp. 29–33 (2016)
Leonova, N.M., Modyaev, A.D., Kolychev, V.D.: Visualization of a product’s life cycles in the common information space on the basis of project management methods. Sci. Vis. 8(5), 26–40 (2016)
Modjaev, A.D., Leonova, N.M.: Information-educational environment with adaptive control of learning process. AIP Conf. Proc. 1797, 020013 (2017)
Szeliski, R: Computer Vision: Algorithms and Applications. Springer Science & Business Media (2010)
Gonzales, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Pearson Education (2008)
Redmon, J., Santosh D., Ross G., Ali F.: You Only Look Once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Ali, F.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Redmon, J., Ali F.: Yolov3: An Incremental Improvement. arXiv preprint arXiv:1804.02767 (2018)
Acknowledgements
This work was supported by Competitiveness Growth Program of the Federal Autonomous Educational Institution of Higher Education National Research Nuclear University MEPhI (Moscow Engineering Physics Institute).
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
Kulik, S.D., Shtanko, A.N. (2020). Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for Intelligent Robotics. In: Misyurin, S., Arakelian, V., Avetisyan, A. (eds) Advanced Technologies in Robotics and Intelligent Systems. Mechanisms and Machine Science, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-33491-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-33491-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33490-1
Online ISBN: 978-3-030-33491-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)