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. 2025 Jun 26;104(10):105484.
doi: 10.1016/j.psj.2025.105484. Online ahead of print.

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. .

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.

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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

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