文章基本信息

标题：Mask R-CNN for Geospatial Object Detection
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作者：Dalal AL-Alimi ; Yuxiang Shao ; Ahamed Alalimi 等
期刊名称：International Journal of Information Technology and Computer Science
印刷版ISSN：2074-9007
电子版ISSN：2074-9015
出版年度：2020
卷号：12
期号：5
页码：63-72
DOI：10.5815/ijitcs.2020.05.05
出版社：MECS Publisher
摘要：Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.
关键词：Mask R-CNN;Faster R-CNN;RoIAlign;object detection;instance segmentation