用三视角融合技术提升蛋鸡密集场景下的计数精度。
TP-MVCC: Tri-plane Multi-view Fusion Model for Silkie Chicken Counting
- 通过三平面投影与特征融合，统一多视角图像信息。
- 在密集遮挡场景下达到95.1%计数准确率。
- 首个真实养殖环境下的丝毛鸡多视角数据集，适合农业智能化研究者。
精准动物计数对智慧农业至关重要，但在拥挤场景中因遮挡和视角有限而困难。为此，我们提出基于三平面的多视角蛋鸡计数模型（TP-MVCC），利用几何投影与三平面融合，将多摄像头特征整合至统一地面平面。该框架提取单视角特征，通过空间变换对齐，并解码出场景级密度图以实现精确计数。此外，我们构建了首个真实养殖环境下丝毛鸡的多视角数据集。实验表明，TP-MVCC显著优于单视角及传统融合方法，在密集遮挡场景中达到95.1%的准确率，展现出在智能农业中的实际应用潜力。
原文摘要 · Abstract (English)
Accurate animal counting is essential for smart farming but remains difficult in crowded scenes due to occlusions and limited camera views. To address this, we propose a tri-plane-based multi-view chicken counting model (TP-MVCC), which leverages geometric projection and tri-plane fusion to integrate features from multiple cameras onto a unified ground plane. The framework extracts single-view features, aligns them via spatial transformation, and decodes a scene-level density map for precise chicken counting. In addition, we construct the first multi-view dataset of silkie chickens under real farming conditions. Experiments show that TP-MVCC significantly outperforms single-view and conventional fusion comparisons, achieving 95.1\% accuracy and strong robustness in dense, occluded scenarios, demonstrating its practical potential for intelligent agriculture.
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