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Object Detection by Tiny-YOLO on TurtleBot3 as an Educational Robot

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Communication and Intelligent Systems (ICCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 689))

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Abstract

By the unceasing efforts of many scientists, machine learning algorithms are increasing quickly with an enhanced object detection performance for the explicit applications on specific devices. This paper investigates the operation of TurtleBot3 as an educational wheeled autonomous robot which Tiny-YOLO algorithm has been applied as a novel application on it. At first, the required knowledge about the kinematics and dynamics of a differential drive robot with constraints is studied, and a graph where the robot is working under some specific conditions is plotted. The robot operating system (ROS) and simultaneous localization and mapping (SLAM) of the TurtleBot3 have been explained. The robot is equipped with a LiDAR sensor and a camera embedded onto it as a sensor fusion SLAM for a more precise type of mapping. Finally, Tiny-YOLO as a machine learning algorithm for object detection is implemented based on the fact that Raspberry Pi could not handle the full YOLO due to its memory capacity limitation, and the results are discussed.

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References

  1. Chernousko F (2017) Locomotion principles for mobile robotic systems. Proc Comput Sci: 213–216

    Google Scholar 

  2. Siegwart R, Nourbakhsh IR, Scaramuzza AD (2004) Introduction to autonomous mobile robots, pp 13–43, 369–393

    Google Scholar 

  3. Moezzi R, Krcmarik D, Hlava J, Cýrus J (2020) Hybrid SLAM modelling of autonomous robot with augmented reality device. Mater Today Proc 32:103–107. https://doi.org/10.1016/j.matpr.2020.03.036

    Article  Google Scholar 

  4. Moezzi R, Krcmarik D, Cýrus J, Bahri H, Koci J (2022) Object detection using Microsoft HoloLens by a single forward propagation CNN. In: Proceedings in adaptation, learning and optimization, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-97196-0_42

  5. Wang W, Lai Q, Fu H, Shen J, Ling H, Yang R (2022) Salient object detection in the deep learning era: an in-depth survey. IEEE Trans Pattern Anal Mach Intell 44(6):3239–3259. https://doi.org/10.1109/TPAMI.2021.3051099

    Article  Google Scholar 

  6. Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 6154–6162. https://doi.org/10.1109/CVPR.2018.00644

  7. Girshick R (2015) Fast R-CNN. Paper presented at the proceedings of the IEEE international conference on computer vision, 2015 international conference on computer vision, ICCV 2015, 1440–1448. https://doi.org/10.1109/ICCV.2015.169

  8. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Paper presented at the advances in neural information processing systems, 2015-Jan, 91–99

    Google Scholar 

  9. Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2019-June, 7944–7953. https://doi.org/10.1109/CVPR.2019.00814

  10. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2016-Dec, 779–788. https://doi.org/10.1109/CVPR.2016.91

  11. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC (2016) SSD: single shot multibox detector. https://doi.org/10.1007/978-3-319-46448-0_2

  12. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. Paper presented at the proceedings—30th IEEE conference on computer vision and pattern recognition, CVPR 2017, 2017-Jan, 6517–6525. https://doi.org/10.1109/CVPR.2017.690

  13. Tumas P, Serackis A (2018) Automated image annotation based on YOLOv3. Paper presented at the 2018 IEEE 6th workshop on advances in information, electronic and electrical engineering, AIEEE 2018—proceedings. https://doi.org/10.1109/AIEEE.2018.8592167

  14. Wai YJ, Yussof ZM, Bin Salim SI, Chuan LK (2018) Fixed point implementation of tiny-yolo-v2 using OpenCL on FPGA. Int J Adv Comput Sci Appl 9(10):506–512

    Google Scholar 

  15. Ma J, Chen L, Gao Z (2018) Hardware implementation and optimization of tiny-YOLO network. https://doi.org/10.1007/978-981-10-8108-8_21

  16. Features of Turtlebot3, Robotis [online]. Available https://emanual.robotis.com/docs/en/platform/turtlebot3/features/#components

  17. ROS—Robot Operating System [online]. Available https://www.generationrobots.com/blog/en/ros-robot-operating-system-2/. Zugriff am 20 2 2022

  18. Hernandez-Mendez S, Hernandez-Mejia C, Herrera Olea DB, Marin-Hernandez A, Rios-Figueroa HV (2021) Path planning simulation of a quadrotor in ROS/Gazebo using RGPPM. Paper presented at the proceedings—2021 international conference on mechatronics, electronics and automotive engineering, ICMEAE 2021, 20–25. https://doi.org/10.1109/ICMEAE55138.2021.00011

  19. Moezzi R, Krcmarik D, Bahri H, Hlava J (2019) Autonomous vehicle control based on HoloLens technology and raspberry pi platform: an educational perspective. Paper presented at the IFAC-PapersOnLine, 52(27), 80–85.https://doi.org/10.1016/j.ifacol.2019.12.737

  20. Gazebo Navigation Simulation, Robotis [online]. Available https://emanual.robotis.com/docs/en/platform/turtlebot3/nav_simulation/. Zugriff am 15 2 2022.

  21. Bonaccorso G (2017) Machine learning algorithms. Packt Publishing, pp 6–23

    Google Scholar 

  22. Fu H, Niu Z, Zhang C, Chen JMAJ (2013) Visual cortex inspired CNN model for feature construction in text analysis. PubMed, Beijing, pp 1–7

    Google Scholar 

  23. Huang R, Pedoeem J, Chen C (2019) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. Paper presented at the proceedings—2018 IEEE international conference on big data. Big data 2018, 2503–2510. https://doi.org/10.1109/BigData.2018.8621865

  24. Lin TY et al (2014) Microsoft COCO: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. ECCV 2014. Lecture notes in computer science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_48

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Author information

Authors and Affiliations

  1. Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Liberec, Czech Republic

    Reza Moezzi & Jindrich Cyrus

  2. Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Liberec, Czech Republic

    Reza Moezzi & Jaroslav Hlava

  3. Faculty of Electrical Engineering and Computer Science, Görlitz University of Applied Sciences, Zittau, Germany

    Adrian Saw & Stefan Bischoff

Authors
  1. Reza Moezzi
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  2. Adrian Saw
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  3. Stefan Bischoff
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  4. Jindrich Cyrus
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  5. Jaroslav Hlava
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Corresponding author

Correspondence to Reza Moezzi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  2. National Institute of Technology Delhi, New Delhi, India

    Vivek Shrivastava

  3. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Kusum Kumari Bharti

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Moezzi, R., Saw, A., Bischoff, S., Cyrus, J., Hlava, J. (2023). Object Detection by Tiny-YOLO on TurtleBot3 as an Educational Robot. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 689. Springer, Singapore. https://doi.org/10.1007/978-981-99-2322-9_47

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