论文标题
遥感图像上的几个射击对象检测
Few-shot Object Detection on Remote Sensing Images
论文作者
论文摘要
在本文中，我们处理遥感图像上对象检测的问题。先前的方法开发了许多基于CNN的深度方法，用于在遥感图像上进行对象检测以及报告在检测性能和效率方面的显着成就。但是，当前基于CNN的方法主要需要大量注释的样本来训练深层神经网络，并且对于看不见的对象类别的概括能力往往有限。在本文中，我们介绍了一种基于射击的学习方法，用于在遥感图像上检测对象检测，其中仅提供了几个注释的示例针对看不见的对象类别。更具体地说，我们的模型包含三个主要组成部分：一个元特征提取器，该元素提取器学会从输入图像中提取特征表示形式，一个重新处理模块，该模块学会从支持图像中自适应为每个特征表示的不同权重分配不同的权重，以及一个边界框预测模块，该模块在重新持续的特征映射上运行对象检测对象检测。我们在Yolov3体系结构上构建了几个弹声对象检测模型，并开发了一个多尺度对象检测框架。两个基准数据集的实验表明，只有少数注释的样本，我们的模型仍然可以在遥感图像上实现令人满意的检测性能，并且模型的性能要比建立的基线模型要好得多。
In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote sensing images and the report remarkable achievements in detection performance and efficiency. However, current CNN-based methods mostly require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories. More specifically, our model contains three main components: a meta feature extractor that learns to extract feature representations from input images, a reweighting module that learn to adaptively assign different weights for each feature representation from the support images, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework. Experiments on two benchmark datasets demonstrate that with only a few annotated samples our model can still achieve a satisfying detection performance on remote sensing images and the performance of our model is significantly better than the well-established baseline models.
