H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network
H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network
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DOI：
10.3390/rs12244192
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发表时间：
2020-12
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通讯作者：
G. Tang;Shibo Liu;Iwao Fujino;Christophe Claramunt;Yide Wang;Shaoyang Men
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文献类型：
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作者：
G. Tang;Shibo Liu;Iwao Fujino;Christophe Claramunt;Yide Wang;Shaoyang Men
从高分辨率光学卫星图像中的船舶检测仍然是应有最佳解决方案的重要任务。本文基于感兴趣区域（ROI）的预选，介绍了一种新型的高分辨率基于图像网络的方法。该预选网络首先从输入图像中识别并提取了感兴趣的区域。为了有效匹配候选船舶，我们的方法的原理是将可疑区域与基于色调，饱和度，价值（HSV）差异的图像区分开来。整个方法是由大型船数据集进行实验的基础，该数据集由Google Earth Images和HRSC2016数据集组成。该实验表明，使用一组遥感图像中相同的重量训练的H-Yolo网络比仅使用一次（Yolo）网络的识别率高19.01％，精度高16.19％。图像预处理后，交叉点比联合（IOU）的值也得到了很大改善。
Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.