Real time object recognition based on YOLO model

Research Article
Open access

Real time object recognition based on YOLO model

Zeyu Guan 1*
  • 1 Nanjing University of Aeronautics and Astronaut    
  • *corresponding author zyguan@nuaa.edu.cn
Published on 26 December 2023 | https://doi.org/10.54254/2753-8818/28/20230450
TNS Vol.28
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-261-9
ISBN (Online): 978-1-83558-262-6

Abstract

With the rapid development of computer technology, the concept of computer vision has been proposed. Since then, many object recognition methods have been developed to lay the foundation for computer vision. Object recognition is vital in various computer vision applications, such as autonomous driving, surveillance systems, robotics, and other areas. The You Only Look Once (YOLO) model has gained significant attention due to its ability to achieve real-time object detection and localization in images and videos. This paper comprehensively reviews real-time object recognition based on the YOLO model. We discuss the YOLO architecture's underlying principles and advantages over traditional object detection methods. Then, according to the article by Joseph Redmon, the inventor of YOLO, the benefits of each version of the YOLO model and the performance optimization compared to the previous work are briefly introduced in the order of release. Furthermore, this paper explores its applications in different domains.

Keywords:

Component, Object Recognition, You Only Look Once Model, Computer Vision

Guan,Z. (2023). Real time object recognition based on YOLO model. Theoretical and Natural Science,28,137-143.
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References

[1]. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”.

[2]. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” arXiv, Jan. 06, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1506.01497

[3]. L. Du, R. Zhang, and X. Wang, “Overview of two-stage object detection algorithms,” J. Phys.: Conf. Ser., vol. 1544, no. 1, p. 012033, May 2020, doi: 10.1088/1742-6596/1544/1/012033.

[4]. K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN.” arXiv, Jan. 24, 2018. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1703.06870

[5]. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger.” arXiv, Dec. 25, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1612.08242

[6]. W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Computer Vision – ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., in Lecture Notes in Computer Science, vol. 9905. Cham: Springer International Publishing, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.

[7]. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection.” arXiv, May 09, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1506.02640

[8]. K. J. Oguine, O. C. Oguine, and H. I. Bisallah, “YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s).” arXiv, Sep. 26, 2022. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/2209.12447

[9]. C.-J. Lin, S.-Y. Jeng, and H.-W. Lioa, “A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO,” Mathematical Problems in Engineering, vol. 2021, pp. 1–10, Nov. 2021, doi: 10.1155/2021/1577614.

[10]. K. J. Oguine, O. C. Oguine, and H. I. Bisallah, “YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s).” arXiv, Sep. 26, 2022. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/2209.12447

Cite this article

Guan,Z. (2023). Real time object recognition based on YOLO model. Theoretical and Natural Science,28,137-143.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation

ISBN：978-1-83558-261-9(Print) / 978-1-83558-262-6(Online)
Editor：Roman Bauer
Conference website: https://www.confmpcs.org/
Conference date: 12 August 2023
Series: Theoretical and Natural Science
Volume number: Vol.28
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”.

[2]. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” arXiv, Jan. 06, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1506.01497

[3]. L. Du, R. Zhang, and X. Wang, “Overview of two-stage object detection algorithms,” J. Phys.: Conf. Ser., vol. 1544, no. 1, p. 012033, May 2020, doi: 10.1088/1742-6596/1544/1/012033.

[4]. K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN.” arXiv, Jan. 24, 2018. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1703.06870

[5]. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger.” arXiv, Dec. 25, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1612.08242

[6]. W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Computer Vision – ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., in Lecture Notes in Computer Science, vol. 9905. Cham: Springer International Publishing, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.

[7]. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection.” arXiv, May 09, 2016. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/1506.02640

[8]. K. J. Oguine, O. C. Oguine, and H. I. Bisallah, “YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s).” arXiv, Sep. 26, 2022. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/2209.12447

[9]. C.-J. Lin, S.-Y. Jeng, and H.-W. Lioa, “A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO,” Mathematical Problems in Engineering, vol. 2021, pp. 1–10, Nov. 2021, doi: 10.1155/2021/1577614.

[10]. K. J. Oguine, O. C. Oguine, and H. I. Bisallah, “YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s).” arXiv, Sep. 26, 2022. Accessed: Jul. 03, 2023. [Online]. Available: http://arxiv.org/abs/2209.12447

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