数字农科院2.0

Cattle-ES3D: A spatiotemporal feature fusion method for detecting tachypnea and salivation behaviors in beef cattle

文献类型： 外文期刊

作者： Fuyang Tian;Liyin Zhang;Ji Zhang;Shuaiyang Zhang;Shakeel Ahmed Soomro;Benhai Xiong;Weizheng Shen;Zhanhua Song;Yinfa Yan;Zhenwei Yu

作者机构：

关键词： Beef cattle;Behavior recognition;Cattle-ES3D;Spatiotemporal feature fusion

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2025 年 239 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Accurate and efficient detection of tachypnea and salivation behavior plays a key role in improving the health management of beef cattle. To address the challenges of low detection accuracy and high computational redundancy in existing algorithms within complex breeding environments, the Cattle-ES3D algorithm was proposed for detecting tachypnea and salivation behaviors in beef cattle. First, a hybrid architecture was proposed, which integrated the Embedded Spatial Pyramid Network (ESP-Net) for multi-scale extraction and SlowFast dual-pathway network to realize the extraction of spatiotemporal features in beef cattle. Second, the Adaptive Spatiotemporal Feature Fusion Synchronization Module (AST-Sync) was designed to achieve adaptive fusion of spatiotemporal features. Finally, a lightweight dynamic detection branch was designed to achieve classification-regression feature spatial alignment and temporal association constraints through a multi-dimensional parameter optimization mechanism driven by spatiotemporal dynamic label assignment. The experimental results showed that the method utilized resulted with spatial and temporal features to enhance the detection accuracy of tachypnea and salivation behaviors in beef cattle, with reduced computational cost. The Cattle-ES3D model achieved a mean Average Precision (mAP) of 93.4 %, GFLOPs of 39.6 and FPS of 33.2. Compared to C3D, I3D, P3D and R(2 + 1)D, Cattle-ES3D improved mAP by 8.2 %, 6.8 %, 3.3 %, and 18.4 % respectively, while reducing GFLOPs by 0.7, 11.8, 7.0, and 8.2 respectively. These results demonstrated that the proposed model provided a robust and high-performance technical solution for intelligent livestock farming.

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