Skip to main content
SpringerLink
Log in

An objectness-aware network for wildlife detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The application of object detectors contributes to the monitoring and protection of wildlife with high accuracy and fast speed. However, the variety of shapes and appearances of wildlife bring difficulties to the detection task, which requires the detection models a better objectness definition and label assignment. This study proposes an objectness-aware YOLO (OA-YOLO) to improve the effect on wildlife detection. First, we redefine the objectness values of training samples and decouple the objectness branch in the detector head. Second, we propose a Natural Breaks Label Assignment (NBLA) algorithm to divide the anchors into positive, negative, and assistant samples automatically based on their objectness values. Third, the assistant samples used to be ignored participate in the classification training in an objectness-related weighted manner to improve the detection accuracy. The experimental results indicate that OA-YOLO improves the mean average precision (mAP) by 6.9% on the challenging wildlife dataset and outperforms the existing approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Beery S, Agarwal A, Cole E, Birodkar V (2021) The iwildcam 2021 competition dataset. arXiv:2105.03494

  2. Bochkovskiy A, Wang CY, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934

  3. Cao J, Tang H, Fang H-S, Shen X, Lu C, Tai Y-W (2019) Cross-domain adaptation for animal pose estimation. In: Proceedings of the IEEE/CVF International conference on computer vision, pp 9498–9507

  4. Chen Y, Bai Y, Zhang W, Mei T (2019) Destruction and construction learning for fine-grained image recognition. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 5157–5166

  5. Choi J, Chun D, Kim H, Lee H-J (2019) Gaussian yolov3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE/CVF International conference on computer vision, pp 502–511

  6. Feng W, Ju W, Li A, Bao W, Zhang J (2019) High-efficiency progressive transmission and automatic recognition of wildlife monitoring images with wisns. IEEE Access 7:161412–161423

    Article  Google Scholar 

  7. Hsu W-Y, Lin W-Y (2020) Ratio-and-scale-aware yolo for pedestrian detection. IEEE Trans Image Process 30:934–947

    Article  Google Scholar 

  8. Ke W, Zhang T, Huang Z, Ye Q, Liu J, Huang D (2020) Multiple anchor learning for visual object detection. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 10206–10215

  9. Kiani Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International conference on computer vision, pp 1135–1143

  10. Kim K, Lee HS (2020) Probabilistic anchor assignment with iou prediction for object detection. Springer

  11. Law H, Deng J (2018) Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 734–750

  12. Li H, Wu Z, Zhu C, Xiong C, Socher R, Davis LS (2020) Learning from noisy anchors for one-stage object detection. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 10588–10597

  13. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International conference on computer vision, pp 2980–2988

  14. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision, pp 21–37. Springer

  15. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 8759–8768

  16. Liu X, Yang T, Yan B (2015) Internet of things for wildlife monitoring. In: 2015 IEEE/CIC International conference on communications in China - Workshops (CIC/ICCC), pp 62–66. https://doi.org/10.1109/ICCChinaW.2015.7961581

  17. Nguyen H, Maclagan SJ, Nguyen TD, Nguyen T, Flemons P, Andrews K, Ritchie EG, Phung D (2017) Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp 40–49. IEEE

  18. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv:1804.02767

  19. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv neural inf process syst, vol 28

  20. Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 10781–10790

  21. Tian Z, Shen C, Chen H, He T (2019) Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International conference on computer vision, pp 9627–9636

  22. Yousif H, Kays R, He Z (2019) Dynamic programming selection of object proposals for sequence-level animal species classification in the wild. IEEE Transactions on Circuits and Systems for Video Technology

  23. Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 9759–9768

  24. Zhang X, Wan F, Liu C, Ji R, Ye Q (2019) Freeanchor: learning to match anchors for visual object detection. Adv Neural Inf Process Syst, vol 32

  25. Zhang S, Wen L, Bian X, Lei Z, Li SZ (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4203–4212

  26. Zhang G, Wong H-C, Lo S-L (2020) Multi-attention network for unsupervised video object segmentation. IEEE Signal Process Lett 28:71–75

    Article  Google Scholar 

  27. Zhou X, Wang D, Krähenbühl P (2019) Objects as points. arXiv:1904.07850

Download references

Acknowledgements

This work was supported by the Key Research and Development Program in Jiangsu Province (No.BE2016739).

Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Xin Lu & Xiaobo Lu

  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, China

    Xin Lu & Xiaobo Lu

  3. Nanjing Enbo Technology Co., Ltd., Nanjing, 210007, China

    Weixuan Li

Authors
  1. Xin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weixuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaobo Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaobo Lu.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, X., Li, W. & Lu, X. An objectness-aware network for wildlife detection. Multimed Tools Appl 83, 7119–7133 (2024). https://doi.org/10.1007/s11042-023-15246-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15246-8

Keywords

Search

Navigation