Skip to main content
SpringerLink
Log in

A Review on YOLOv8 and Its Advancements

  • Conference paper
  • First Online:
Data Intelligence and Cognitive Informatics (ICDICI 2023)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Object detection is a crucial task in computer vision that has its application in various fields like robotics, medical imaging, surveillance systems, and autonomous vehicles. The newest version of the YOLO model, YOLOv8 is an advanced real-time object detection framework, which has attracted the attention of the research community. Of all the popular object identification methods and machine-learning models such as Faster RCNN, SSD, and RetinaNet, YOLO is the most popularly known method in terms of accuracy, speed, and efficiency. This research study provides an analysis of YOLO v8 by highlighting its innovative features, improvements, applicability in different environments, and a detailed comparison of its performance metrics to other versions and models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Ulatralytics GitHub repository https://github.com/ultralytics/ultralytics.

  2. 2.

    Roboflow blog https://blog.roboflow.com/whats-new-in-yolov8/.

References

  1. Deng J, Xuan X, Wang W et al (2020) A review of research on object detection based on deep learning. J Phys: Conf Ser 1684:012028. https://doi.org/10.1088/1742-6596/1684/1/012028

    Article  Google Scholar 

  2. Bianchini M, Simic M, Ghosh A, Shaw RN (2022) In machine learning for robotics applications. Springer Verlag, Singapore, S.l.

    Google Scholar 

  3. Agrawal T, Kirkpatrick C, Imran K, Figus M (2020) Automatically detecting personal protective equipment on persons in images using amazon recognition. Amazon, 2020. Retrieved from https://aws.amazon.com/blogs/machine-learning/automatically-detecting-personal-protective-equipment-on-persons-in-images-using-amazon-rekognition/. (accessed April 27, 2023)

  4. Rasouli A, Tsotsos JK (2019) Autonomous vehicles that interact with pedestrians: a survey of theory and practice. IEEE Trans Intell Transp Syst 21:900–918. https://doi.org/10.1109/TITS.2019.2901817

    Article  Google Scholar 

  5. Martinez-Martin E, del Pobil AP (2017) Object detection and recognition for assistive robots: experimentation and implementation. IEEE Robot Autom Mag 24:123–138. https://doi.org/10.1109/MRA.2016.2615329

    Article  Google Scholar 

  6. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, n.d. https://doi.org/10.1109/cvpr.2001.990517

  7. Dalal N, Triggs B Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). https://doi.org/10.1109/cvpr.2005.177

  8. Felzenszwalb PF, Ross BG, David M (2010) Cascade object detection with deformable part models. In: IEEE computer society conference on computer vision and pattern recognition. https://doi.org/10.1109/cvpr.2010.5539906

  9. Nash R (2015) An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458

  10. Girshick, Ross, Jeff Donahue, Trevor Darrell, and Jitendra Malik. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. https://doi.org/10.1109/cvpr.2014.81.

  11. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824

    Article  Google Scholar 

  12. Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision (ICCV). https://doi.org/10.1109/iccv.2015.169

  13. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  14. Lin TY, Piotr D, Ross G, Kaiming H, Bharath H, Serge B (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2017.106

  15. Redmon J, Divvala S, Girshick R, Farhadi A (2016) you only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/CVPR.2016.91

  16. Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot multibox detector. Comput Vision—ECCV 216:21–37. https://doi.org/10.1007/978-3-319-46448-0_2

  17. Redmon J, Ali F (2017) Yolo9000: better, faster, stronger. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2017.690

  18. Redmon J, Ali F (2018) “YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767

  19. Bochkovskiy A, Chien-Yao W, Hong-Yuan ML (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

  20. Jocher G (2020) YOLOv5 by Ultralytics (Version 7.0). Comput Softw. https://doi.org/10.5281/zenodo.3908559

    Article  Google Scholar 

  21. Li C, Lulu L, Hongliang J, Kaiheng W, Yifei G, Liang L, Zaidan K, Qingyuan L, Meng C, Weiqiang N, Yiduo L (2022) YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976

  22. Wang CY, Alexey B, Hong-Yuan ML (2022) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696

  23. Ultralytics (2023) YOLOv8 Docs. Retrieved from https://docs.ultralytics.com/. accessed April 27, 2023

  24. Jocher G, Chaurasia A, Qiu J (2023) YOLO by Ultralytics (Version 8.0.0). Computer software. GitHub. Retrieved from https://github.com/ultralytics/ultralytics.

  25. Range K, Jocher G (2023) Brief summary of YOLOv8 model structure. GitHub Issue. Retrieved from https://github.com/ultralytics/ultralytics/issues/189. accessed April 27, 2023

  26. Bochkovskiy A, Wang C, Liao HM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

  27. Liu W, Hasan I, Liao S (2023) Center and scale prediction: anchor-free approach for pedestrian and face detection. Pattern Recogn 135:109071. https://doi.org/10.1016/j.patcog.2022.109071

    Article  Google Scholar 

  28. Lin TY, Maire M, Belongie S et al (2014) Microsoft coco: common objects in context. Computer Vision – ECCV, pp 740–755. https://doi.org/10.1007/978-3-319-10602-1_48

  29. Common Objects in Context (2023) COCO. Retrieved from https://cocodataset.org/. accessed April 27, 2023

  30. Ciaglia F, Zuppichini FS, Guerrie P, McQuade M, Solawetz J (2022) Roboflow 100: a rich, multi-domain object detection benchmark. arXiv preprint arXiv:2211.13523

  31. Roboflow 100: A new object detection benchmark (2023) RF100. Retrieved from https://www.rf100.org/ accessed April 27, 2023.

  32. Solawetz JF (2023) What is YOLOv8? the ultimate guide. Blog post. Retrieved from https://blog.roboflow.com/whats-new-in-yolov8/ accessed April 27, 2023

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, 522237, India

    Mupparaju Sohan, Mupparaju Sohan, Thotakura Sai Ram & Ch. Venkata Rami Reddy

Authors
  1. Mupparaju Sohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thotakura Sai Ram
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ch. Venkata Rami Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mupparaju Sohan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, GITAM University, Bangalore, Karnataka, India

    I. Jeena Jacob

  2. ISOM Department, University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

  3. Gdańsk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sohan, M., Sai Ram, T., Rami Reddy, C.V. (2024). A Review on YOLOv8 and Its Advancements. In: Jacob, I.J., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. ICDICI 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-7962-2_39

Download citation

Publish with us

Policies and ethics

Search

Navigation