GroupNet:Learning to group corner for object detection in remote sensing imagery  

作　　者：Lei NI Chunlei HUO Xin ZHANG Peng WANG Zhixin ZHOU 

机构地区：[1]Space Engineering University,Beijing 101416,China [2]National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [3]Beijing Institute of Remote Sensing,Beijing 100192,China

出　　处：《Chinese Journal of Aeronautics》2022年第6期273-284,共12页中国航空学报（英文版）

基　　金：supported by Natural Science Foundation of China (No. 62071466)

摘　　要：Due to the attractive potential in avoiding the elaborate definition of anchor attributes,anchor-free-based deep learning approaches are promising for object detection in remote sensing imagery.Corner Net is one of the most representative methods in anchor-free-based deep learning approaches.However,it can be observed distinctly from the visual inspection that the Corner Net is limited in grouping keypoints,which significantly impacts the detection performance.To address the above problem,a novel and effective approach,called Group Net,is presented in this paper,which adaptively groups corner specific to the objects based on corner embedding vector and corner grouping network.Compared with the Corner Net,the proposed approach is more effective in learning the semantic relationship between corners and improving remarkably the detection performance.On NWPU dataset,experiments demonstrate that our Group Net not only outperforms the Corner Net with an AP of 12.8%,but also achieves comparable performance to considerable approaches with 83.4%AP.

关 键 词：CornerNet Feature representation Multi-dimension embedding Object detection Remote sensing 

分 类 号：P237[天文地球—摄影测量与遥感]