Author
Listed:
- Yaobo Zhang
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Linwei Chen
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Hongfei Chen
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Tao Liu
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Jinlin Liu
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Qiuhong Zhang
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Mingduo Yan
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Kaiyue Zhao
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Shixiu Zhang
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
- Xiuguo Zou
(College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)
Abstract
The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition.
Suggested Citation
Yaobo Zhang & Linwei Chen & Hongfei Chen & Tao Liu & Jinlin Liu & Qiuhong Zhang & Mingduo Yan & Kaiyue Zhao & Shixiu Zhang & Xiuguo Zou, 2025.
"DualHet-YOLO: A Dual-Backbone Heterogeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House,"
Agriculture, MDPI, vol. 15(14), pages 1-29, July.
Handle:
RePEc:gam:jagris:v:15:y:2025:i:14:p:1504-:d:1700520
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:14:p:1504-:d:1700520. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.