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BMF-CNN: an object detection method based on multi-scale feature fusion in VHR remote sensing images

机译：BMF-CNN：一种基于多尺度特征融合的VHR遥感图像目标检测方法

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摘要

Object detection in very-high-resolution (VHR) remote sensing images is one of the important technical means in many fields. In recent years, conventional object detection methods have been completely replaced by Convolutional Neural Network (CNN)-based methods, which are more accurate and efficient. However, most current CNN-based methods applied in VHR image sets have certain defects: (1) Scale preference is common in the framework designs, and the representation ability of feature maps for large and small objects is quite different, so accuracy promotion can hardly be made comprehensively in the detection of different objects. (2) The scale difference of the objects leads to training difficulties. (3) Some high-precision methods require high hardware costs, and the overall frameworks lack practicality. To address such problems, we propose a new object detection method in this paper, namely Balanced Multi-Scale Fusion-based CNN (BMF-CNN). It is a redesigned two-stage detection framework according to the region-based object detection methods, which enabled the detection accuracy of both large and small objects to reach a high level. Through the evaluation in the open VHR remote sensing image sets, we found that BMF-CNN showed a better integrative performance than the current mainstream detection methods.
机译：超高分辨率（VHR）遥感图像中的目标检测是许多领域的重要技术手段之一。近年来，传统的目标检测方法已经完全被基于卷积神经网络（CNN）的方法所取代，该方法更加准确和高效。但是，目前在VHR图像集中使用的大多数基于CNN的方法都存在某些缺陷：（1）比例尺偏好在框架设计中很常见，并且特征图对于大型和小型物体的表示能力差异很大，因此很难提高精度在检测不同物体时进行全面的检测。 （2）物体的比例差异导致训练困难。 （3）一些高精度方法需要较高的硬件成本，并且整个框架缺乏实用性。为了解决这些问题，本文提出了一种新的目标检测方法，即基于平衡多尺度融合的CNN（BMF-CNN）。它是根据基于区域的对象检测方法重新设计的两阶段检测框架，使大型和小型对象的检测精度均达到较高水平。通过对开放式VHR遥感影像集的评估，我们发现BMF-CNN的集成性能比当前的主流检测方法更好。

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    《Remote sensing letters》 |2020年第3期|215-224|共10页
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    Chinese Acad Sci Acad Optoelect 9th Deng Zhuang South Rd Beijing Peoples R China;

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