基于掩模R-CNN算法的机库检测

Asli Nur Ömeroglu, Nida Kumbasar, E. A. Oral, I. Y. Özbek
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引用次数: 9

摘要

本研究采用Mask R-CNN算法对高分辨率机场(民用和军用)卫星图像中的机库进行检测。虽然在卫星图像中检测建筑物是一种常见的做法,但由于一些机库被伪装成不同的大小,因此给检测算法带来了困难。Mask R-CNN是一种实例对象分割算法,对图像中的对象进行检测,分别标记每个对象的边界框及其在边界框中的像素信息。在本研究中,准备了来自各个空军基地的300个样本的高分辨率机库数据集,并使用Mask R-CNN实现了85%的平均精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mask R-CNN Algoritması ile Hangar Tespiti Hangar Detection with Mask R-CNN Algorithm
In this study, the detection of hangars in high resolution airport (civil and military) satellite images was performed using Mask R-CNN algorithm. Although the detection of buildings in the satellite images is a common practice, being some of the hangars camouflaged in different sizes cause difficulty for the detection algorithms. In Mask R-CNN, an instance object segmentation algorithm, objects in the images are detected, bounding box of each object as well as their pixel information with in the box are marked separately. In this study, high resolution hangar data set with 300 samples, collected from various air bases, was prepared, and an 85% average precision is achieved using Mask R-CNN.
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