MACA-Net: Mamba-Driven Adaptive Cross-Layer Attention Network for Multi-Behavior Recognition in Group-Housed Pigs

Zhixiong Zeng, Zaoming Wu, Runtao Xie, Kai Lin, Shenwen Tan, Xinyuan He and Yizhi Luo ()
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Zhixiong Zeng: Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zaoming Wu: School of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Runtao Xie: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Kai Lin: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Shenwen Tan: Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xinyuan He: Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yizhi Luo: Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 9, 1-27

Abstract: The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, lying on the belly, lying on the side, and standing. The model incorporates a Mamba Global–Local Extractor (MGLE) Module, which leverages Mamba to capture global dependencies while preserving local details through convolutional operations and channel shuffle, overcoming Mamba’s limitation in retaining fine-grained visual information. Additionally, an Adaptive Multi-Path Attention (AMPA) mechanism integrates spatial-channel attention to enhance feature focus, ensuring robust performance in complex environments and low-light conditions. To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. Experimental results demonstrate that MACA-Net achieves a precision of 83.1% and mAP of 85.1%, surpassing YOLOv8n by 8.9% and 4.4%, respectively. Furthermore, MACA-Net significantly reduces parameters by 48.4% and FLOPs by 39.5%. When evaluated in comparison to leading detectors such as RT-DETR, Faster R-CNN, and YOLOv11n, MACA-Net demonstrates a consistent level of both computational efficiency and accuracy. These findings provide a robust validation of the efficacy of MACA-Net for intelligent livestock management and welfare-driven breeding, offering a practical and efficient solution for modern pig farming.

Keywords: pig behaviors; Mamba; deep learning; object detection (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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