CountMamba用多方向扫描模型提升植物计数精度
CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting
- 设计多方向状态空间组，模拟不同计数视角
- 在玉米雄穗、小麦穗等任务上达到领先性能
- 适合农业视觉中的密集目标计数场景
植物计数在育种、播种、栽培、施肥、授粉、产量估算和收获等农业环节中至关重要。受人类在高分辨率图像中通过逐行扫描计数的启发，我们探索使用状态空间模型（SSMs）生成计数结果的可能性。本文提出CountMamba，构建多个计数专家并行从不同方向扫描图像。具体而言，设计多方向状态空间组以多种顺序处理图像块序列，模拟不同计数视角；同时引入全局-局部自适应融合模块，以样本自适应方式聚合多方向全局特征与卷积神经网络分支提取的局部特征。大量实验表明，所提方法在玉米雄穗、小麦穗和高粱头计数等多种植物计数任务上表现优异，具有竞争力。
原文摘要 · Abstract (English)
Plant counting is essential in every stage of agriculture, including seed breeding, germination, cultivation, fertilization, pollination yield estimation, and harvesting. Inspired by the fact that humans count objects in high-resolution images by sequential scanning, we explore the potential of handling plant counting tasks via state space models (SSMs) for generating counting results. In this paper, we propose a new counting approach named CountMamba that constructs multiple counting experts to scan from various directions simultaneously. Specifically, we design a Multi-directional State-Space Group to process the image patch sequences in multiple orders and aim to simulate different counting experts. We also design Global-Local Adaptive Fusion to adaptively aggregate global features extracted from multiple directions and local features extracted from the CNN branch in a sample-wise manner. Extensive experiments demonstrate that the proposed CountMamba performs competitively on various plant counting tasks, including maize tassels, wheat ears, and sorghum head counting.
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