用光流合成中间帧，让无人机少飞一圈也能生成清晰农田图
Ortho-Fuse: Orthomosaic Generation for Sparse High-Resolution Crop Health Maps Through Intermediate Optical Flow Estimation
- 通过光流预测生成中间图像，增强图像间匹配点
- 将拼接所需重叠度从70%-80%降至50%-60%
- 适合资源有限的农户用无人机做精准农业监测
基于AI的作物健康监测系统在数据采集速度和成本控制方面优于传统方法。然而，农民广泛采用仍受限于稀疏航拍影像生成正射影像的难题。传统摄影测量重建需70%-80%的图像重叠以建立足够特征对应，而资源受限的AI系统难以稳定达到该阈值，导致重建质量下降，削弱用户对自主监测技术的信任。本文提出Ortho-Fuse，一种基于光流的框架，通过中间帧估计合成连续航拍图像间的过渡画面，人工增加特征对应关系，从而提升几何重建精度。实验表明，该方法可降低20%的最小重叠要求。我们进一步分析了精准农业中的采纳障碍，提出促进AI监测系统集成的路径。
原文摘要 · Abstract (English)
AI-driven crop health mapping systems offer substantial advantages over conventional monitoring approaches through accelerated data acquisition and cost reduction. However, widespread farmer adoption remains constrained by technical limitations in orthomosaic generation from sparse aerial imagery datasets. Traditional photogrammetric reconstruction requires 70-80\% inter-image overlap to establish sufficient feature correspondences for accurate geometric registration. AI-driven systems operating under resource-constrained conditions cannot consistently achieve these overlap thresholds, resulting in degraded reconstruction quality that undermines user confidence in autonomous monitoring technologies. In this paper, we present Ortho-Fuse, an optical flow-based framework that enables the generation of a reliable orthomosaic with reduced overlap requirements. Our approach employs intermediate flow estimation to synthesize transitional imagery between consecutive aerial frames, artificially augmenting feature correspondences for improved geometric reconstruction. Experimental validation demonstrates a 20\% reduction in minimum overlap requirements. We further analyze adoption barriers in precision agriculture to identify pathways for enhanced integration of AI-driven monitoring systems.
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