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Real-time object detection and segmentation technology: an analysis of the YOLO algorithm

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Abstract

In this paper, the YOLO (You Only Look Once) algorithm, which is a representative algorithm for real-time object detection and segmentation technology, is analyzed according to in order of development. As its name suggests, the YOLO algorithm can detect objects with a single forward pass, making possible fast and accurate object detection and segmentation. This paper explores the characteristics and history of the YOLO algorithm. The performance of the YOLO algorithm is evaluated using the COCO (Common Objects in Context) data set. By far the most difficult aspect of deep learning is preparing the training data, and the data applicable to each field of application is severely limited. Despite these limitations, the YOLO model still has a substantially faster processing speed than other conventional models and continues to be in widespread use. Each version of the YOLO algorithm has adopted various ideas and techniques for further performance improvements, presenting researchers with new directions for resolving problems in object detection. These advances will continue, and the YOLO algorithm will provide important insights into the ways we understand and recognize images and video.

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Acknowledgements

This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and ICT, the Republic of Korea (No. 2021R1C1C1009219, No. 2021R1F1A1063298).

Funding

This study was supported by Ministry of Science and ICT, South Korea, No. 2021R1C1C1009219, Sun Young Kim, No. 2021R1F1A1063298, Chang Ho Kang.

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Authors and Affiliations

  1. Department of Mechanical Systems Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea

    Chang Ho Kang

  2. School of Mechanical Engineering, Kunsan National University, Gunsan, 54150, Republic of Korea

    Sun Young Kim

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  1. Chang Ho Kang
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  2. Sun Young Kim
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Correspondence to Sun Young Kim.

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Kang, C.H., Kim, S.Y. Real-time object detection and segmentation technology: an analysis of the YOLO algorithm. JMST Adv. 5, 69–76 (2023). https://doi.org/10.1007/s42791-023-00049-7

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  • DOI: https://doi.org/10.1007/s42791-023-00049-7

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