skip to main content
10.1145/3672919.3673000acmotherconferencesArticle/Chapter ViewAbstractPublication PagescsaideConference Proceedingsconference-collections
research-article

A Training Strategy of Flying Bird Object Detection Model Based on Improved Self-Paced Learning Algorithm

Published: 24 July 2024 Publication History

Abstract

In order to avoid the impact of hard samples on the training process of the Flying Bird Object Detection model (FBOD model, in our previous work, we designed the FBOD model according to the characteristics of flying bird objects in surveillance video), the Self-Paced Learning method with Easy Sample Prior Based on Confidence (SPL-ESP-BC), a new model training strategy, is proposed. Firstly, the loss-based Minimizer Function in Self-Paced Learning (SPL) is improved, and the confidence-based Minimizer Function is proposed, which makes it more suitable for one-class object detection tasks. Secondly, to give the model the ability to judge easy and hard samples at the early stage of training by using the SPL strategy, an SPL strategy with Easy Sample Prior (ESP) is proposed. The FBOD model is trained using the standard training method with easy samples first, then the SPL method with all samples is used to train it. Combining the strategy of the ESP and the Minimizer Function based on confidence, the SPL-ESP-BC model training strategy is proposed. Using this strategy to train the FBOD model can make it to learn the characteristics of the flying bird object in the surveillance video better, from easy to hard. The experimental results show that compared with the standard training method that does not distinguish between easy and hard samples, the AP50 of the FBOD model trained by the SPL-ESP-BC is increased by 2.1%.

References

[1]
X. Shi, J. Hu, X. Lei and S. Xu, "Detection of Flying Birds in Airport Monitoring Based on Improved YOLOv5," 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2021, pp. 1446-1451.
[2]
Tianhang WU, Xiaoyan LUO and Qunyu XU. A new skeleton based flying bird detection method for low-altitude air traffic management [J]. Chinese Journal of Aeronautics. vol. 31, no. 11, pp. 2149-2164, 2018.
[3]
H. Zhao, D. Cai, Z. Liang and Y. Wang, "An Improved Method for Farm Birds Detection Based on YOLOv5s," 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Shanghai, China, 2022, pp. 183-187.
[4]
Shivam Goel, Santosh Bhusal, Matthew E Taylor and Manoj Karkee, "Detection and localization of birds for Bird Deterrence using UAS," 2017 ASABE Annual International Meeting 1701288. (
[5]
R. Yoshihashi, R. Kawakami, M. Iida, T. Naemura, "Bird detection and species classification with time-lapse images around a wind farm: Dataset construction and evaluation" in Wind Energy, vol. 20, no. 12, pp. 1983-1995, 2017.
[6]
Christopher J.W. McClure, Luke Martinson and Taber D. Allison, " Automated monitoring for birds in flight: Proof of concept with eagles at a wind power facility," Biological Conservation, vol. 224, pp. 26-33, 2018.
[7]
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston, "Curriculum learning", In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09), Association for Computing Machinery, New York, NY, USA, 2009. pp. 41–48.
[8]
X. Wang, Y. Chen and W. Zhu, "A Survey on Curriculum Learning," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4555-4576, 1 Sept. 2022.
[9]
Kumar, M., Packer, Ben and Koller, Daphne, " Self-Paced Learning for Latent Variable Models", Advances in Neural Information Processing Systems, 2010, pp. 1189-1197.
[10]
Z. Sun, Z. Hua, H. Li and Y. Li, "Flying Bird Object Detection Algorithm in Surveillance Video", arXiv e-prints, 2024.
[11]
Z. Zheng, "Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation," in IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 8574-8586, Aug. 2022.
[12]
Fan, Yanbo, "Self-paced learning: an implicit regularization perspective." National Conference on Artificial Intelligence 2016.
[13]
M. Everingham, L. Van Gool. C. K. I. Williams. J. Winn, and A. Zisserman, "The pascal visual object classes (voc) challenge," International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, Jun 2010.

Index Terms

  1. A Training Strategy of Flying Bird Object Detection Model Based on Improved Self-Paced Learning Algorithm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    CSAIDE '24: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
    March 2024
    676 pages
    ISBN:9798400718212
    DOI:10.1145/3672919
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 July 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    CSAIDE 2024

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 16
      Total Downloads
    • Downloads (Last 12 months)16
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media