论文标题
用于快速卫星对象检测的软标签
Soft Labels for Rapid Satellite Object Detection
论文作者
论文摘要
图像分类中的软标签是图像真实分类的矢量表示。在本文中，我们在卫星对象检测的背景下研究了软标签。我们建议使用检测作为新的软标签数据集的基础。创建高质量模型的大部分努力是收集和注释培训数据。如果我们可以使用模型为我们生成数据集，那么我们不仅可以快速创建数据集，还可以补充现有的开源数据集。使用Xview数据集的子集，我们训练Yolov5模型来检测汽车，飞机和船只。然后，我们使用该模型为第二个训练集生成软标签，然后将其与原始模型进行比较。我们表明，软标签可用于训练模型，该模型几乎与对原始数据训练的模型一样准确。
Soft labels in image classification are vector representations of an image's true classification. In this paper, we investigate soft labels in the context of satellite object detection. We propose using detections as the basis for a new dataset of soft labels. Much of the effort in creating a high-quality model is gathering and annotating the training data. If we could use a model to generate a dataset for us, we could not only rapidly create datasets, but also supplement existing open-source datasets. Using a subset of the xView dataset, we train a YOLOv5 model to detect cars, planes, and ships. We then use that model to generate soft labels for the second training set which we then train and compare to the original model. We show that soft labels can be used to train a model that is almost as accurate as a model trained on the original data.
