Proceedings of International Conference on Applied Innovation in IT
2023/03/09, Volume 11, Issue 1, pp.127-132

Research of Methods to Increase the Efficiency of the Object Detection System on the Raspberry Pi Platform


Daria Koshutina and Svetlana Antoshchuk


Abstract: The work is devoted to the consideration and solution of the problems of object detection efficiency. This article analyzes object detection methods. The existing methods and systems of object detection are considered. On the basis of the researched methods, prospects and further directions for the development of object detection programs are defined. This research is relevant in today’s world, because smart devices, robots and robotic systems are increasingly being used to improve life. Therefore, the object detection system is an important part of robotics and automation. The development of a real-time object detection algorithm on the Raspberry Pi platform is described. The method of automatic detection and recognition of objects is described. To check the effectiveness of the methods, a system was designed and implemented, which is a camera connected to Raspberry Pi using the algorithm developed during the work. The problem consists in creating algorithms and methods to improve the response time and accuracy of object detection in real time. The system was created on the basis of already existing research results, refinement and implementation of the methods proposed in them In the course of the study, the results of the development of the object detection system based on the developed algorithms were presented and their effectiveness was investigated.

Keywords: Robotics, Raspberry Pi, Python, Machine Learning, Deep Learning, Convolutional Neural Networks, Computer Vision, Object Detection, Single-Shot MultiBox Detector, R-CNN.

DOI: 10.25673/101928

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References:

  1. D. H. Ballard and C. M. Brown, Computer Vision, New Jersey, USA: Prentice Hall, 1982.
  2. S. Dasiopoulou, V. Mezaris, I. Kompatsiaris, V.-K. Papastathis, and M. G. Strintzis,”Knowledgeassisted semantic video object detection,” IEEE Trans. Cir. Syst. for Video Techn., vol. 15, no. 10, pp. 1210-1224, 2005.
  3. Y. Jang, H. Gunes, and I. Patras, “Registration-free face-ssd: Single shot analysis of smiles, facial attributes, and affect in the wild,” Comp. Vis. Image Und., vol. 182, pp. 17-29, 2019
  4. M. Babichev and V. Lytvynenko, Eds., Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. Boston, MA: Springer Cham, 2022, pp. XVII, 721.
  5. G. Stockman and L. G. Shapiro, Computer Vision. Upper Saddle River, NJ, USA: Prentice Hall PTR, 2001
  6. Z. Jiang, L. Zhao, S. Li, and Y. Jia, “Real-time object detection method based on improved YOLOv4-tiny,” arXiv preprint arXiv:2011.04244, 2020
  7. D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification,” in Twenty-second int. joint conf. on AI. Citeseer, 2011.
  8. H. Zhou, F. Jiang, and H. Lu, “Ssda-yolo: Semisupervised domain adaptive yolo for cross-domain object detection,” Comp. Vis. and Im. Und., vol. 229, p. 103649, 2023
  9. M. A. Feroz, M. Sultana, M. R. Hasan, A. Sarker, P. Chakraborty, and T. Choudhury, “Object detection and classification from a real-time video using ssd and yolo models,” in Comp. Int. in Patt. Rec.: Proc. of CIPR 2021. Springer, 2022, pp. 37–47
  10. S. Kanimozhi, G. Gayathri, and T. Mala, “Multiple real-time object identification using single shot multibox detection,” in 2019 ICCIDS. IEEE, 2019, pp. 1-5.
  11. K. Wadhwa and J. K. Behera, “Accurate real-time object detection using SSD,” Int. Res. J. of Eng. and Techn., vol. 7, no. 5, 2020.
  12. X. Xie, G. Cheng, J. Wang, X. Yao, and J. Han, “Oriented r-cnn for object detection,” in Proceedings of the IEEE/CVF Int. Conf. on Comp. Vis., 2021, pp. 3520-3529.
  13. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th Eur. Conf., Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, 2016, pp. 21-37.
  14. O. Ferm, “Real-time object detection on Raspberry Pi 4: Fine-tuning a SSD model using tensorflow and web scraping,” 2020.
  15. L. Liu and M. T. Özsu, Eds., Mean Average Precision. Boston, MA: Springer US, 2009, pp. 1703-1703.


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DOI: http://dx.doi.org/10.25673/115729


        

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