论文标题
空中图像中的对象检测
Object Detection in Aerial Imagery
论文作者
论文摘要
多年来，自然图像中的对象检测取得了显着的结果。但是，由于几个挑战，例如高分辨率图像，规模变化，阶级失衡等，在空中对象检测中尚未观察到类似的进展。我们显示了ISAID数据集对两阶段，一阶段和基于注意力的对象探测器的性能。此外，我们描述了针对不同模型进行的一些修改和分析 - a）在两个阶段检测器中：引入了基于加权注意的FPN，类平衡采样器和密度预测头。 b）在一个阶段检测器中：使用的加权局灶性损失并引入了FPN。 c）在基于注意力的探测器中：比较单一的多尺度注意力并证明了不同骨架的效果。最后，我们展示了一项比较研究，突出了空中图像设置中不同模型的利弊。
Object detection in natural images has achieved remarkable results over the years. However, a similar progress has not yet been observed in aerial object detection due to several challenges, such as high resolution images, instances scale variation, class imbalance etc. We show the performance of two-stage, one-stage and attention based object detectors on the iSAID dataset. Furthermore, we describe some modifications and analysis performed for different models - a) In two stage detector: introduced weighted attention based FPN, class balanced sampler and density prediction head. b) In one stage detector: used weighted focal loss and introduced FPN. c) In attention based detector: compare single,multi-scale attention and demonstrate effect of different backbones. Finally, we show a comparative study highlighting the pros and cons of different models in aerial imagery setting.
