在高分辨率航拍图像中跨时间偏移可靠检测微小目标
Reliable Detection of Minute Targets in High-Resolution Aerial Imagery across Temporal Shifts
AI总结：
本文通过迁移学习初始化的Faster R-CNN架构，解决高分辨率航拍图像中跨时间偏移的微小目标检测问题，验证了模型在不同时间间隔和环境变化下的鲁棒性与性能一致性。
摘要：
高效的无人机作物检测对于扩大精准农业的应用至关重要，但仍然具有挑战性，因为目标规模小且环境变化大。本文通过迁移学习初始化的Faster R-CNN架构，解决稻田中秧苗检测的问题。为克服在高分辨率航拍图像中检测微小目标的特定困难，我们构建了一个显著的无人机数据集进行训练，并严格评估模型的泛化能力。具体而言，我们验证了在三个不同时间间隔获取的三个不同测试集上的性能，从而评估模型对变化成像条件的鲁棒性。我们的实证结果表明，迁移学习不仅促进了农业情境中目标检测模型的快速收敛，而且即使在图像采集领域变化的情况下也能保持一致的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
