A group object detection framework for remote sensing image object perception
Optical remote sensing is a widely used technology in aerospace reconnaissance and geological exploration.Visible light images captured by this technology provide a wealth of information and have important applications in intelligence gathering,object monitoring,and situational forecasting.Considerable progress in remote sensing image object perception has been achieved,particularly in ship and airplane detection.However,technical challenges,including with large object-scale variations and numerous small objects,in remote sensing image object perception remain.Existing work has mainly focused on improving boundary box representations,and single-object detection models fail to fully exploit spatial correlation information from surrounding or similar objects.To address the inherent inefficiency of existing remote sensing image object detection algorithms that detect different objects independently,this paper proposes a novel detection framework called group object detection.By detecting the state information of a group object,our framework alleviates problems,such as insufficient perception information and poor reliability of single-object perception,generating reliable multi-object detection results.This paper introduces a concept of group objects and proposes an automated annotation scheme for group objects.By analyzing existing labels on a public dataset,the proposed scheme obtains annotated information with group object labels without manual annotation.Based on the automated annotation of group targets,a group target detection algorithm is presented,which enhances single-object detection results by utilizing the spatial constraints of group objects.Experimental results on the DOTA dataset,a widely-used remote sensing object detection benchmark,demonstrate that the proposed group target detection algorithm outperforms state-of-the-art methods.
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