论文标题
具有嘈杂且稀疏的地理通量（完整版）的遥感图像中的强大对象检测（完整版）
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)
论文作者
论文摘要
最近，航空车和卫星的遥感图像的可用性不断改善。为了对此类数据进行自动解释，基于深度学习的对象检测器实现了最新的性能。但是，已建立的对象探测器需要培训的完整，精确和正确的边界框注释。为了为对象探测器创建必要的训练注释，可以将图像与其他来源的数据（例如GPS传感器定位的兴趣点）进行地理化并结合使用。不幸的是，这种组合通常会导致差的物体定位和缺失的注释。因此，使用此类数据的训练对象探测器通常会导致检测性能不足。在本文中，我们提出了一种新颖的方法，用于训练对象探测器具有极为嘈杂和不完整的注释。我们的方法基于教师学习框架和一个校正模块，以不精确和缺失的注释。因此，我们的方法易于使用，可以与任意对象检测器结合使用。我们证明，我们的方法将标准检测器提高了37.1 \％$ ap_ {50} $在嘈杂的现实世界遥感数据集上。此外，我们的方法在具有合成噪声的两个数据集上获得了巨大的性能提高。代码可在\ url {https://github.com/mxbh/robust_object_detection}中获得。
Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art performance. However, established object detectors require complete, precise, and correct bounding box annotations for training. In order to create the necessary training annotations for object detectors, imagery can be georeferenced and combined with data from other sources, such as points of interest localized by GPS sensors. Unfortunately, this combination often leads to poor object localization and missing annotations. Therefore, training object detectors with such data often results in insufficient detection performance. In this paper, we present a novel approach for training object detectors with extremely noisy and incomplete annotations. Our method is based on a teacher-student learning framework and a correction module accounting for imprecise and missing annotations. Thus, our method is easy to use and can be combined with arbitrary object detectors. We demonstrate that our approach improves standard detectors by 37.1\% $AP_{50}$ on a noisy real-world remote-sensing dataset. Furthermore, our method achieves great performance gains on two datasets with synthetic noise. Code is available at \url{https://github.com/mxbh/robust_object_detection}.
