DualHet-YOLO: A Dual-Backbone Heterogeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House

Yaobo Zhang, Linwei Chen, Hongfei Chen, Tao Liu, Jinlin Liu, Qiuhong Zhang, Mingduo Yan, Kaiyue Zhao, Shixiu Zhang and Xiuguo Zou ()
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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

Agriculture, 2025, vol. 15, issue 14, 1-29

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.

Keywords: floor-raised chicken behavior recognition; model lightweighting; YOLO-based target detection; dual-path feature map extraction; TriAxis unified detection head (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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