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摘要: 目标检测技术是光学遥感图像理解的基础问题, 具有重要的应用价值. 本文对遥感图像目标检测算法发展进行了梳理和分析. 首先阐述了遥感图像目标检测的特点和挑战; 之后系统总结了典型的检测方法, 包括早期的基于手工设计特征的算法和现阶段基于深度学习的方法, 对于深度学习方法首先介绍了典型的目标检测模型, 进而针对遥感图像本身的难点详细梳理了优化改进方案; 接着介绍了常用的检测数据集, 并对现有方法的性能进行比较; 最后对现阶段问题进行总结并对未来发展趋势进行展望.Abstract: Object detection in optical aerial images is a fundamental problem in the field of remote sensing and shows great importance in the application. The performance of the early hand-craft-feature algorithm is limited, while deep learning is the primary method for object detection at present. However, due to the characteristics of the remote sensing image itself, it is difficult for the existing detection algorithms to perform well on these images. In this paper, we first describe the characteristics and challenges of the object detection task in aerial images. We then summarize the typical detection methods, including early hand-craft feature extraction methods and current deep learning methods, especially for the deep learning algorithm enhancement upon the characteristics of the aerial images. Then the commonly used detection datasets are introduced, and the performances of the existing methods are compared. Finally, we summarize the current deficiencies and analyze the trends of future studies.
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Key words:
- Remote sensing /
- object detection /
- hand-craft features /
- deep learning /
- dataset
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表 1 水平框检测算法性能对比
Table 1 Performance comparison of horizontal box detection algorithms
算法 主干 改进模块 mAP 超大覆盖 方向多样 尺度过小 密集分布 形状差异 尺度变化 外观模糊 复杂背景 NWPU VHR-10数据集 RICNN[25] AlexNet √ 73.10 R-P-Faster-RCNN[134] VGG-16 76.50 Def.R-FCN[87] Res-101 √ 79.10 Def.Faster-RCNN[88] Res-50 √ √ 84.40 RICADet[111] ZF √ √ 87.12 RDAS512[72] VGG-16 √ √ 89.50 Multi-Scale CNN[98] VGG-16 √ 89.60 CAD-Net[108] Res-101 √ √ 91.50 SCRDet[80] Res-101 √ √ √ √ 91.75 DOTA数据集 FR-H[53] Res-101 √ 60.46 SBL[135] Res-50 √ 64.77 FMSSD[73] VGG-16 √ √ √ 72.43 ICN[84] Res-101 √ √ 72.50 IoU-Adaptive[110] Res-101 √ √ 72.72 EFR[106] VGG-16 √ √ 73.49 SCRDet[80] Res-101 √ √ √ √ √ 75.35 FADet[89] Res-101 √ √ √ √ 75.38 MFIAR-Net t[117] Res-152 √ √ √ 76.07 Mask OBB[105] ResX-101 √ √ √ 76.98 A2RMNet[90] Res-101 √ √ √ 78.45 OWSR[109] Res-101 √ √ √ 78.79 Parallel Cascade R-CNN[101] ResX-101 √ √ √ 78.96 DM-FPN[99] Res-101 √ √ √ 79.27 SCRDet++[82] Res-101 √ √ √ √ √ 79.35 表 2 旋转框检测算法性能对比
Table 2 Performance comparison of rotated box detection algorithms
算法 主干 改进模块 mAP 超大覆盖 尺度过小 密集分布 形状差异 尺度变化 外观模糊 复杂背景 边界问题 FR-O[119] Res-101 √ 52.93 IENet[91] Res-101 √ √ √ √ 57.14 TOSO[96] Res-101 √ √ √ 57.52 R-DFPN[83] Res-101 √ √ 57.94 R2CNN[121] Res-101 √ 60.67 RRPN[120] Res-101 √ 61.01 Axis Learning[95] Res-101 √ √ √ 65.98 ICN[84] Res-101 √ √ 68.20 RADet[107] ResX-101 √ √ √ √ √ 69.09 RoI-Transformer[86] Res-101 √ √ 69.56 P-RSDet[92] Res-101 √ √ 69.82 CAD-Net[108] Res-101 √ √ √ 69.90 O2-DNet[93] HG-104 √ √ 71.04 AOOD[103] Res-101 √ √ √ 71.18 Cascade-FF[116] Res-152 √ √ √ √ 71.80 SCRDet[80] Res-101 √ √ √ √ √ √ 72.61 SARD[124] Res-101 √ √ √ 72.95 GLS-Net[118] Res-101 √ √ √ 72.96 DRN[97] HG-104 √ √ 73.23 FADet[89] Res-101 √ √ √ √ 73.28 MFIAR-Net[117] Res-152 √ √ √ 73.49 R3Det[81] Res-152 √ √ √ 73.74 RSDet[126] Res-152 √ √ √ 74.10 Gliding Vertex[123] Res-101 √ √ √ 75.02 Mask OBB[105] ResX-101 √ √ √ √ 75.33 FFA[104] Res-101 √ √ 75.70 APE[122] ResX-101 √ √ √ 75.75 CSL[125] Res-152 √ √ √ 76.17 OWSR[109] Res-101 √ √ √ 76.36 SCRDet++[82] Res-101 √ √ √ √ √ √ 76.81 -
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