Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Oct;104(10):105484.
doi: 10.1016/j.psj.2025.105484. Epub 2025 Jun 26.

YOLOv8-DuckPluck: A lightweight target detection model for cherry valley duck feather pecking site detection

Xuliang Duan  1 , Pengsen Wang  2 , Yue Hu  3 , Haochen Li  4 , Shuoxian Yang  5 , Yunshan Zhu  6
Affiliations

YOLOv8-DuckPluck: A lightweight target detection model for cherry valley duck feather pecking site detection

Xuliang Duan et al. Poult Sci. 2025 Oct.

Abstract

The pecking phenomenon in poultry can lead to stress responses, feather loss, and even death. Therefore, precise and timely monitoring of pecking behavior in poultry is crucial for modern precision animal husbandry. Existing deep learning-based object detection models often face challenges such as slow processing speed, large parameter quantities and bulky model sizes when dealing with complex environments characterized by high density and multiple targets. To address these issues, this paper proposes a cherry valley duck feather pecking site detection model based on YOLOv8. Firstly, a novel lightweight multi-scale feature extraction module, NeoMSM-C2f, is integrated into the backbone network to enhance the model's multi-scale feature extraction capability. Secondly, DyHead is employed as the detection head, which adaptively adjusts the detection strategy based on the dynamic variations of input features. Finally, using YOLOv8_nS as the student model and YOLOv8_sT as the teacher model, knowledge distillation is applied to further improve the detection accuracy of YOLOv8_nS, resulting in the YOLOv8-DuckPluck model, which balances both detection speed and accuracy. Experimental results show that the YOLOv8-DuckPluck model significantly outperforms the baseline model YOLOv8_n, with the mAP increasing from 85.51 % to 90.24 %, and the detection speed rising from 66.98 f/s to 76.3 f/s. This demonstrates that the model meets the real-time processing requirements for feather pecking site detection task of Cherry Valley ducks, achieves high detection accuracy, reduces deployment costs and complexity, and exhibits strong practical applicability.

Keywords: Dense object detection; Feather Pecking; Knowledge distillation; Lightweight; Precision animal husbandry.

PubMed Disclaimer

Conflict of interest statement

Disclosures The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig. 1
Partial dataset display.
Fig 2
Fig. 2
Improved YOLOv8 structure diagram.
Fig 3
Fig. 3
Hierarchical structure diagram of NeoMSM-C2f.
Fig 4
Fig. 4
DSA structure diagram.
Fig 5
Fig. 5
DRA structure diagram.
Fig 6
Fig. 6
DyHead structure diagram.
Fig 7
Fig. 7
DyHead three-dimensional flow chart.
Fig 8
Fig. 8
BCKD distillation strategy process.
Fig 9
Fig. 9
Comparison of the receptive fields of the network backbone before and after improvement.
Fig 10
Fig. 10
Grad-CAM visualization effect comparison (a) YOLOv8_n (b) YOLOv8_n+NeoMSM-C2f (c) YOLOv8_n+NeoMSM-C2f+DyHead (d) YOLOv8-DuckPluck.
See this image and copyright information in PMC

References

    1. Ahn S., Hu S.X., Damianou A., Lawrence N.D., Dai Z. Proc. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019. Variational information distillation for knowledge transfer.
    1. Azizat, M. H., .E. A. M. Graat, A. Youssef, and S. Neethirajan. 2022. Laying hen's behavioural responses and activity indexes to different light colours. agriRxiv:20220347348.
    1. Birkl P., Bharwani A., Kjaer J., Kunze W., McBride P., Forsythe P., Harlander-Matauschek A. Differences in cecal microbiome of selected high and low feather-pecking laying hens. Poult. Sci. 2018;97:3009–3014. - PMC - PubMed
    1. Chen Y., Wen X., Zhang Y., He Q. FPC: filter pruning via the contribution of output feature map for deep convolutional neural networks acceleration. Knowl.-Based Syst. 2022;238
    1. Chen Y., Wen X., Zhang Y., Shi W. CCPrune: collaborative channel pruning for learning compact convolutional networks. Neurocomputing. 2021;451:35–45.