ABSTRACT

Object location is a fundamental yet challenging problem in object detection. In the remote sensing image, different imaging projection directions make the same object have various rotation angles, and in some scenes, the object distribution is relatively dense. Most of the existing deep learning-based object detection algorithms utilize horizontal bounding box to locate objects, which causes inaccurate location of the objects with dense distribution or arbitrary direction, thus leading to the detection misses. In this paper, we propose an arbitrary-angle bounding box based object location and embed it into the Faster R-CNN, developing a new framework called Rotated Faster R-CNN (R-FRCNN) for object detection in remote sensing image. In R-FRCNN, we specially improve anchor ratios to adapt to the objects like ship with large aspect ratio and increase the weights of the horizontal bounding box regression to reduce the interference of the arbitrary-angle bounding box on the horizontal bounding box prediction. Comprehensive experiments on a public dataset and a self-assembled dataset (which we make publically available) show the superior performance of our method compared to standalone state-of-the-art object detectors.

摘要

对象定位是对象检测中的一个基本但具有挑战性的问题。在遥感图像中，不同的成像投影方向会使同一物体具有不同的旋转角度，并且在某些场景中，物体的分布相对密集。现有的大多数基于深度学习的物体检测算法都利用水平边界框来定位物体，这导致物体的密集分布或任意方向的定位不准确，从而导致检测遗漏。在本文中，我们提出了一种基于任意角度边界框的目标定位并将其嵌入到Faster R-CNN中，从而开发了一种新的框架，称为“旋转Faster R-CNN（R-FRCNN）”，用于遥感图像中的目标检测。在R-FRCNN中，我们特别提高了锚定比，以适应大宽高比的船舶，并增加了水平边界框回归的权重，以减少任意角度边界框对水平边界框预测的干扰。在公共数据集和自组装数据集（我们公开提供）上的综合实验表明，与独立的最新对象检测器相比，我们的方法具有优越的性能。