ABSTRACT

In recent years, the emergence of convolutional neural networks (CNN) has greatly promoted the development of the object detection field, and many CNN-based detectors have achieved excellent performance on object detection in remote sensing images. To accurately locate the target, oriented bounding box (OBB) is usually used in remote sensing objects, such as the angle-based OBB, to represent the target. Nevertheless, the critical loss instability caused by the periodicity of the angle is always difficult to solve. In this paper, we propose a novel strategy called the Center-Guide points (CGP) box method that uses the guide points to locate the target, which breaks the limit of the angle-based thinking pattern to solve the critical loss instability problem. To be specific, we define a new guide-points selection rule and prediction structure, which replaces the traditional method of using angle values to indicate the direction. Furthermore, we propose the matching method of centre points and guide points, which is a box decoding method that matches the object and the corresponding guide points. Finally, an attention learning module called the Gaussian Center-Line (GC-L) Attention module based on the Gaussian centre-line is proposed to improve the accuracy of guide points. These strategies are applied to the key point detection framework and tested on three classical-oriented object remote sensing datasets. The results show that our method is effective and competitive.

摘要

近年来，卷积神经网络（CNN）的出现极大地推动了物体检测领域的发展，许多基于CNN的检测器在遥感图像中的物体检测上取得了优异的性能。为了准确定位目标，定向边界框（Oriented bounding box，OBB）通常用于遥感对象，如基于角度的OBB，来表示目标。然而，由角度周期性引起的临界损耗不稳定性始终难以解决。在本文中，我们提出了一种称为中心引导点（CGP）框方法的新策略，它使用引导点定位目标，打破了基于角度的思维模式的限制，解决了临界损失不稳定性问题。具体来说，我们定义了一个新的引导点选择规则和预测结构，它取代了使用角度值来指示方向的传统方法。此外，我们提出了中心点和引导点的匹配方法，这是一种匹配对象和相应引导点的框解码方法。最后，提出了一种基于高斯中心线的注意力学习模块，称为高斯中心线（GC-L）注意力模块，以提高引导点的准确性。这些策略应用于关键点检测框架，并在三个面向经典的对象遥感数据集上进行了测试。结果表明，我们的方法有效且具有竞争力。这是一种匹配对象和相应引导点的框解码方法。最后，提出了一种基于高斯中心线的注意力学习模块，称为高斯中心线（GC-L）注意力模块，以提高引导点的准确性。这些策略应用于关键点检测框架，并在三个面向经典的对象遥感数据集上进行了测试。结果表明，我们的方法有效且具有竞争力。这是一种匹配对象和相应引导点的框解码方法。最后，提出了一种基于高斯中心线的注意力学习模块，称为高斯中心线（GC-L）注意力模块，以提高引导点的准确性。这些策略应用于关键点检测框架，并在三个面向经典的对象遥感数据集上进行了测试。结果表明，我们的方法有效且具有竞争力。