用迁移学习提升无人机稻苗检测精度，跨时间鲁棒性强
Reliable Detection of Minute Targets in High-Resolution Aerial Imagery across Temporal Shifts
- 基于迁移学习的Faster R-CNN模型用于稻苗检测
- 在三个不同时段数据集上均保持稳定性能
- 适合需要跨时间检测小目标的农业应用
通过无人机实现高效作物检测对发展精准农业至关重要，但因目标尺度小和环境变化仍具挑战。本文针对稻田中秧苗的检测问题，采用基于迁移学习初始化的Faster R-CNN架构。为应对高分辨率航拍图像中微小目标检测难题，我们构建了一个大规模无人机数据集用于训练，并在三个不同时间采集的测试集上严格评估模型泛化能力，以检验其对成像条件变化的鲁棒性。实验结果表明，迁移学习不仅加速了农业场景下目标检测模型的收敛，还在图像采集存在领域偏移时仍保持一致性能。
原文摘要 · Abstract (English)
Efficient crop detection via Unmanned Aerial Vehicles is critical for scaling precision agriculture, yet it remains challenging due to the small scale of targets and environmental variability. This paper addresses the detection of rice seedlings in paddy fields by leveraging a Faster R-CNN architecture initialized via transfer learning. To overcome the specific difficulties of detecting minute objects in high-resolution aerial imagery, we curate a significant UAV dataset for training and rigorously evaluate the model's generalization capabilities. Specifically, we validate performance across three distinct test sets acquired at different temporal intervals, thereby assessing robustness against varying imaging conditions. Our empirical results demonstrate that transfer learning not only facilitates the rapid convergence of object detection models in agricultural contexts but also yields consistent performance despite domain shifts in image acquisition.
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