论文标题
obbstacking：用于遥感对象检测的合奏方法
OBBStacking: An Ensemble Method for Remote Sensing Object Detection
论文作者
论文摘要
集合方法是结合多种模型以实现出色性能的可靠方法。但是，研究集合方法在遥感对象检测方案中的应用大多被忽略了。出现了两个问题。首先，遥感对象检测的一个独特特征是对象的定向边界框（OBB）和多个OBB的融合需要进一步的研究注意。其次，使用广泛的深度学习对象检测器为每个检测到的对象提供了一个分数，以作为信心的指标，但是如何在集合方法中有效使用这些指标仍然是一个问题。试图解决这些问题，本文提出了与OBB兼容的合奏方法，并以学习的方式结合了检测结果。这种合奏方法有助于在挑战轨道\ textit {高分辨率光学图像中的细粒对象识别}中排名第一，该{\ textIt {2021 Gaofen挑战在自动化高分辨率的地球观察图像解释}中均具有特征。 DOTA数据集和Fair1m数据集的实验表明，分析了Obbstacking的性能以及Obbstacking的功能。
Ensemble methods are a reliable way to combine several models to achieve superior performance. However, research on the application of ensemble methods in the remote sensing object detection scenario is mostly overlooked. Two problems arise. First, one unique characteristic of remote sensing object detection is the Oriented Bounding Boxes (OBB) of the objects and the fusion of multiple OBBs requires further research attention. Second, the widely used deep learning object detectors provide a score for each detected object as an indicator of confidence, but how to use these indicators effectively in an ensemble method remains a problem. Trying to address these problems, this paper proposes OBBStacking, an ensemble method that is compatible with OBBs and combines the detection results in a learned fashion. This ensemble method helps take 1st place in the Challenge Track \textit{Fine-grained Object Recognition in High-Resolution Optical Images}, which was featured in \textit{2021 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation}. The experiments on DOTA dataset and FAIR1M dataset demonstrate the improved performance of OBBStacking and the features of OBBStacking are analyzed.
