论文标题
遥感对象检测的深度积极学习
Deep Active Learning for Remote Sensing Object Detection
论文作者
论文摘要
最近，CNN对象探测器在遥感图像上达到了很高的精度，但需要大量的劳动和时间成本。在本文中，我们提出了一种新的基于不确定性的主动学习，可以选择带有更多信息的图像，并且检测器仍然可以使用训练图像的一小部分来达到高性能。我们的方法不仅分析了对象的分类不确定性，以找到最不自信的对象，而且还考虑了他们的回归不确定性来声明异常值。此外，我们提出了两个额外的权重，以克服遥感数据集中的两个困难，类不平衡和图像对象的差异。我们在DOTA数据集上试验了我们的主动学习算法，将Centernet作为对象检测器。我们只有一半的图像就获得了与全面监督相同的性能。我们甚至在最不自信的图像上以55％的图像和增加重量来覆盖全面监督。
Recently, CNN object detectors have achieved high accuracy on remote sensing images but require huge labor and time costs on annotation. In this paper, we propose a new uncertainty-based active learning which can select images with more information for annotation and detector can still reach high performance with a fraction of the training images. Our method not only analyzes objects' classification uncertainty to find least confident objects but also considers their regression uncertainty to declare outliers. Besides, we bring out two extra weights to overcome two difficulties in remote sensing datasets, class-imbalance and difference in images' objects amount. We experiment our active learning algorithm on DOTA dataset with CenterNet as object detector. We achieve same-level performance as full supervision with only half images. We even override full supervision with 55% images and augmented weights on least confident images.
