[go: up one dir, main page]

Skip to main content

Efficient Object Detection, Segmentation, and Recognition Using YOLO Model

  • Conference paper
  • First Online:
Accelerating Discoveries in Data Science and Artificial Intelligence I (ICDSAI 2023)

Abstract

This chapter presents a study on efficient object detection, segmentation, and recognition using the YOLO (You Only Look Once) model. The YOLOv3 algorithm is used for object detection and recognition, while contour segmentation is used for object segmentation. The study includes experiments on various images, and the results show that the proposed approach achieves high accuracy and speed in detecting and recognizing objects. The contour segmentation technique also provides precise segmentation of objects in the images. The study demonstrates the effectiveness of the YOLO model in object detection, segmentation, and recognition, and its potential for real-time applications.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 199.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
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

Similar content being viewed by others

References

  1. A. Vahab, M.S. Naik, P.G. Raikar, S. Prasad, Applications of object detection system. IRJET 6(4), 4186–4192 (2019)

    Google Scholar 

  2. Y. Li, H. Wang, L.M. Dang, T.N. Nguyen, D. Han, A. Lee, I. Jang, H. Moon, A deep learning-based hybrid framework for object detection and recognition in autonomous driving. IEEE Access 8, 194228–194239 (2020). https://doi.org/10.1109/ACCESS.2020.3033289

    Article  Google Scholar 

  3. R.B. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CoRR abs/1311.2524 (2013). arXiv:1311.2524

  4. J. Redmon, S. K. Divvala, R. B. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, CoRR abs/1506.02640 (2015). arXiv:1506.02640

  5. T. Diwan, G. Anirudh, J.V. Tembhurne, Object detection using yolo: Challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82(6), 9243–9275 (2023). https://doi.org/10.1007/s11042-022-13644-y

    Article  Google Scholar 

  6. S. Gothane, A practice for object detection using yolo algorithm. Int. J. Sci. Res. Comput. Sci. Eng Inf. Technol. 7(2), 268–272 (2021)

    Article  Google Scholar 

  7. A. Morbekar, A. Parihar, R. Jadhav, Crop disease detection using yolo. INCET 2020, 1–5 (2020). https://doi.org/10.1109/INCET49848.2020.9153986

    Article  Google Scholar 

  8. N. M. Krishna, R. Y. Reddy, M. S. C. Reddy, K. P. Madhav, G. Sudham, Object Detection and Tracking Using Yolo, 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1–7. https://doi.org/10.1109/ICIRCA51532.2021.9544598

  9. Y. Kim, H. Bang, Introduction to kalman filter and its applications, in Introduction and Implementations of the Kalman Filter, IntechOpen, ed. by F. Govaers, (Rijeka, 2018., Ch. 2). https://doi.org/10.5772/intechopen.80600

  10. A. Sonavane, R. Kohar, Dental cavity detection using yolo, in Proceedings of Data Analytics and Management, ed. by D. Gupta, Z. Polkowski, A. Khanna, S. Bhattacharyya, O. Castillo, (Springer, Singapore, 2022), pp. 141–152

    Chapter  Google Scholar 

  11. R. Nagpal, S. Long, S. Jahagirdar, W. Liu, S. Fazackerley, R. Lawrence, A. Singh, An application of deep learning for sweet cherry phenotyping using yolo object detection (2023). https://doi.org/10.48550/ARXIV.2302.06698

  12. S. Zhao, F. You, Vehicle detection based on improved yolov3 algorithm (2020) pp. 76–79. https://doi.org/10.1109/ICITBS49701.2020.00024

  13. R. Latha, G. Sreekanth, R. Rajadevi, S. Nivetha, K. Kumar, V. Akash, S. Bhuvanesh, P. Anbarasu, Fruits and vegetables recognition using yolo. ICCCI 2022, 1–6 (2022). https://doi.org/10.1109/ICCCI54379.2022.9740820

    Article  Google Scholar 

  14. A. Balmik, S. Barik, A. Nandy, A robust object recognition using modified yolov5 neural network, arXiv preprint arXiv:YYYY.MMDD (Mar 2023)

    Google Scholar 

  15. K. Saranya, S. Vijayashaarathi, C.S. Christel, R.N. Kumar, Object recognition using FPGA and TINY YOLO (AIP Conference Proceedings 2725 (1), 040002 (04 2023). arXiv. https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0125143/16783696/040002_1_online.pdf). https://doi.org/10.1063/5.0125143

  16. T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, P. Dollár, Microsoft coco: Common Objects in Context (2014). https://doi.org/10.48550/ARXIV.1405.0312

  17. P.-L. Shui, W.-C. Zhang, Noise-robust edge detector combining isotropic and anisotropic Gaussian Kernels. Pattern Recogn. 45(2), 806–820 (2012). https://doi.org/10.1016/j.patcog.2011.07.020

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siddharth Swarup Rautaray .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, A., Rautaray, S.S. (2024). Efficient Object Detection, Segmentation, and Recognition Using YOLO Model. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence I. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-031-51167-7_25

Download citation

Publish with us

Policies and ethics