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                    光學遙感圖像目標檢測算法綜述

                    聶光濤 黃華

                    聶光濤,  黃華.  光學遙感圖像目標檢測算法綜述.  自動化學報,  2021,  47(8): 1749?1768 doi: 10.16383/j.aas.c200596
                    引用本文: 聶光濤,  黃華.  光學遙感圖像目標檢測算法綜述.  自動化學報,  2021,  47(8): 1749?1768 doi: 10.16383/j.aas.c200596
                    Nie Guang-Tao,  Huang Hua.  A survey of object detection in optical remote sensing images.  Acta Automatica Sinica,  2021,  47(8): 1749?1768 doi: 10.16383/j.aas.c200596
                    Citation: Nie Guang-Tao,  Huang Hua.  A survey of object detection in optical remote sensing images.  Acta Automatica Sinica,  2021,  47(8): 1749?1768 doi: 10.16383/j.aas.c200596

                    光學遙感圖像目標檢測算法綜述

                    doi: 10.16383/j.aas.c200596
                    基金項目: 國家自然科學基金(61936011)資助
                    詳細信息
                      作者簡介:

                      聶光濤:北京理工大學計算機科學與技術學院博士研究生. 2015年獲得西安工業大學電子信息工程學院學士學位. 2017年獲得北京理工大學自動化學院碩士學位. 主要研究方向為計算機視覺, 目標檢測和機器學習. E-mail: nieguangtao@bit.edu.cn

                      黃華:北京師范大學人工智能學院教授. 分別于1996年和2006年獲得西安交通大學學士學位和博士學位. 主要研究方向為圖像視頻處理, 計算攝像學和計算機圖形學. 本文通信作者. E-mail: huahuang@bnu.edu.cn

                    A Survey of Object Detection in Optical Remote Sensing Images

                    Funds: Supported by National Natural Science Foundation of China (61936011)
                    More Information
                      Author Bio:

                      NIE Guang-Tao Ph. D. candidate at the School of Computer Science and Technology, Beijing Institute of Technology. He received the his bachelor degree from the School of Electronics and Information Engineering, Xi' an Technological University in 2015, and the master degree from the School of Automation, Beijing Institute of Technology in 2017. His research interest covers computer vision, object detection, and machine learning

                      HUANG Hua Professor at the School of Artificial Intelligence, Beijing Normal University. He received his bachelor and Ph. D. degrees from Xi' an Jiaotong University, in 1996 and 2006, respectively. His research interest covers image and video processing, computational photography, and computer graphics. Corresponding author of this paper

                    • 摘要:

                      目標檢測技術是光學遙感圖像理解的基礎問題, 具有重要的應用價值. 本文對遙感圖像目標檢測算法發展進行了梳理和分析. 首先闡述了遙感圖像目標檢測的特點和挑戰; 之后系統總結了典型的檢測方法, 包括早期的基于手工設計特征的算法和現階段基于深度學習的方法, 對于深度學習方法首先介紹了典型的目標檢測模型, 進而針對遙感圖像本身的難點詳細梳理了優化改進方案; 接著介紹了常用的檢測數據集, 并對現有方法的性能進行比較; 最后對現階段問題進行總結并對未來發展趨勢進行展望.

                    • 圖  1  遙感圖像目標檢測的特點與挑戰

                      Fig.  1  Characteristics and challenges of object detection in remote sensing images

                      圖  2  選擇性搜索方法流程

                      Fig.  2  The process of selective search method

                      圖  3  水平框檢測與旋轉框檢測對比

                      Fig.  3  Comparison of horizontal detection results and rotated detection results

                      圖  4  旋轉框參數表示方案

                      Fig.  4  Parameter representation of rotated boxes

                      圖  5  邊界突變問題示意說明

                      Fig.  5  Illustration of boundary mutation

                      表  1  水平框檢測算法性能對比

                      Table  1  Performance comparison of horizontal box detection algorithms

                      算法主干改進模塊mAP
                      超大覆蓋方向多樣尺度過小密集分布形狀差異尺度變化外觀模糊復雜背景
                      NWPU VHR-10數據集
                      RICNN[25]AlexNet73.10
                      R-P-Faster-RCNN[134]VGG-1676.50
                      Def.R-FCN[87]Res-10179.10
                      Def.Faster-RCNN[88]Res-5084.40
                      RICADet[111]ZF87.12
                      RDAS512[72]VGG-1689.50
                      Multi-Scale CNN[98]VGG-1689.60
                      CAD-Net[108]Res-10191.50
                      SCRDet[80]Res-10191.75
                      DOTA數據集
                      FR-H[53]Res-10160.46
                      SBL[135]Res-5064.77
                      FMSSD[73]VGG-1672.43
                      ICN[84]Res-10172.50
                      IoU-Adaptive[110]Res-10172.72
                      EFR[106]VGG-1673.49
                      SCRDet[80]Res-10175.35
                      FADet[89]Res-10175.38
                      MFIAR-Net t[117]Res-15276.07
                      Mask OBB[105]ResX-10176.98
                      A2RMNet[90]Res-10178.45
                      OWSR[109]Res-10178.79
                      Parallel Cascade R-CNN[101]ResX-10178.96
                      DM-FPN[99]Res-10179.27
                      SCRDet++[82]Res-10179.35
                      下載: 導出CSV

                      表  2  旋轉框檢測算法性能對比

                      Table  2  Performance comparison of rotated box detection algorithms

                      算法主干改進模塊mAP
                      超大覆蓋尺度過小密集分布形狀差異尺度變化外觀模糊復雜背景邊界問題
                      FR-O[119]Res-10152.93
                      IENet[91]Res-10157.14
                      TOSO[96]Res-10157.52
                      R-DFPN[83]Res-10157.94
                      R2CNN[121]Res-10160.67
                      RRPN[120]Res-10161.01
                      Axis Learning[95]Res-10165.98
                      ICN[84]Res-10168.20
                      RADet[107]ResX-10169.09
                      RoI-Transformer[86]Res-10169.56
                      P-RSDet[92]Res-10169.82
                      CAD-Net[108]Res-10169.90
                      O2-DNet[93]HG-10471.04
                      AOOD[103]Res-10171.18
                      Cascade-FF[116]Res-15271.80
                      SCRDet[80]Res-10172.61
                      SARD[124]Res-10172.95
                      GLS-Net[118]Res-10172.96
                      DRN[97]HG-10473.23
                      FADet[89]Res-10173.28
                      MFIAR-Net[117]Res-15273.49
                      R3Det[81]Res-15273.74
                      RSDet[126]Res-15274.10
                      Gliding Vertex[123]Res-10175.02
                      Mask OBB[105]ResX-10175.33
                      FFA[104]Res-10175.70
                      APE[94]ResX-10175.75
                      CSL[125]Res-15276.17
                      OWSR[109]Res-10176.36
                      SCRDet++[82]Res-10176.81
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2020-07-27
                    • 錄用日期:  2020-12-01
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                    • 刊出日期:  2021-08-20

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