基于深度学习的光学遥感图像目标检测研究进展

doi:10.11959/j.issn.1000-436x.2022071

Abstract

Abstract:

Object detection is the core issue in the interpretation of optical remote sensing images, and it is widely used in fields such as intelligence reconnaissance, target monitoring, and disaster rescue.Firstly, combined with the research progress of deep learning optical remote sensing image object detection algorithms, the two types of algorithms based on candidate regions and regression analysis were reviewed.Secondly, the improvement of object detection algorithms for four types of common task-specific scenes were summarized, including rotating objects, small objects, multi-scales, and dense objects.Then, combined with commonly used remote sensing image data sets, the performance of different algorithms was compared and analyzed.Finally, the issues worthy of attention in remote sensing image object detection in the future were prospected, and ideas for follow-up related research were provided.

Key words: optical remote sensing image, object detection, deep learning, convolutional neural network

CLC Number:

TP391.41

Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI. Research progress of deep learning-based object detection of optical remote sensing image[J]. Journal on Communications, 2022, 43(5): 190-203.

Figures/Tables 8

References 118

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文献[21-22]	引入特征融合模块，在准确提取深层网络特征的同时简化计算空间复杂度
文献[23-25]	采用区域上下文信息增强目标特征与对应场景之间的联系
文献[26]	调整特征图尺度变换的方式，增强遥感目标检测的特征表征能力	针对一般遥感目标特征表示阶段改进
文献[27]	固定角度旋转进行数据增强，调整特征图尺度变换的方式
文献[28]	浅层卷积特征提取时使用正则化器
文献[29]	引入特征共享集成机制
文献[30]	提出位置敏感平衡策略
文献[31]	提出选择性搜索匹配策略
文献[32]	提出旋转不敏感RPN，有效提高了对遥感图像中多方向目标的检测效果
文献[33]	提出一种改进的NMS模块，以更好地进行目标区域筛选
文献[34]	提出一种兴趣区域特征金字塔池化模块	针对一般遥感目标感兴趣区域生成与处理阶段改进
文献[35]	提出一种ROI形变器
文献[36]	提出一种ROI排序方法
文献[37]	提出一种ROI旋转策略
文献[38]	将ROI Pooling层与不同交并比加权损失值相结合以提高目标区域定位精度
文献[40]	提出了一种结合掩模预测及语义分割算法，使ROI中保留更多目标空间特征向量
文献[41]	计算旋转IoU损失
文献[42]	计算联合交集IoU损失
文献[43]	设计了比例约束NMS算法，能够筛除遥感目标预选框中的错误结果	针对一般遥感目标提升定位精度改进
文献[44]	提出了衰减NMS算法，能够解决多种遥感目标不同尺度重叠的问题

文献	算法亮点	适用范围
文献[52-54]	在特殊应用背景下的成功探索
文献[55]	设计特征增强模块，能够提升目标的特征表达能力
文献[56]	设计特征精细化模块，能够实现目标特征的细致区分
文献[57]	结合空洞卷积，有效提升YOLO算法针对大目标的精度提升	对检测速度有更高要求的快速遥感目标检测场景
文献[58]	引入多感受域算法，提高了目标特征提取能力
文献[59-60]	引入注意力机制实现精度与速度间的最佳权衡
文献[61]	采取密集连接网络以增强目标特征提取能力
文献[62-66]	分别针对小目标、多尺度、多模态等专项遥感检测场景定制化改进
文献[67]	引入空间和通道注意力模块以筛选融合特征信息
文献[68]	结合空洞卷积与特征融合策略，能够增强浅层特征的语义信息
文献[69]	设计新的编码-解码模块，结合空间和通道注意力级联结构
文献[70]	采取连续两次回归的策略，使检测结果更加精确	对检测速度与精度间有均衡要求的一般遥感目标检测场景
文献[71]	通过定向边界框生成角度修正参数
文献[72]	通过旋转边界框学习遥感目标的正确角度方向
文献[73]	利用密集连接网络提取特征
文献[74]	结合激活函数CReLU和特征金字塔网络，能够提升网络的浅层特征的利用率
文献[75]	利用浅层信息并增大小目标训练权重以提升特征传递效率

文献	算法亮点	适用范围
文献[76]	随机角度旋转以进行数据增强
文献[77-78]	有序四边形表示法学习矩形各边的偏移
文献[79]	结合多层特征与锚定采样的特征融合网络
文献[80]	监督空间注意力机制与多步检测子网络级联相结合
文献[81-82]	将旋转角度回归转化为分类问题并限制分类结果的范围
文献[83]	2个任意方向高斯分布的Wasserstein距离作为旋转IoU损失	针对遥感旋转目标的改进
文献[84]	提出自适应调整的神经元感受野模块
文献[85]	提出目标中线以进行旋转角度回归
文献[86]	提出学生T-分布将分类任务与旋转角度回归相关联
文献[87]	提出掩模分支预测下获取目标旋转边界框
文献[88]	添加正则化项约束训练样本在前后的特征表示
文献[89]	减小网络降采样的倍数，并将浅层与深层特征融合拼接	针对遥感小目标的改进
文献[90]	在浅层特征图中聚合上下文信息，并增加小目标训练权重
文献[91]	构建包含不同尺度信息的特征融合架构
文献[92]	寻找合适的ROI尺寸进行多尺度特征映射与融合
文献[93]	将视觉注意力网络引入多尺度信息融合模块
文献[94]	结合全局注意力模块将多尺度特征图输入显著性金字塔	针对遥感多尺度目标的改进
文献[95]	提出定向引导的锚点框生成网络以生成合适的锚点框
文献[96]	利用特征金字塔进行不同尺度目标信息的提取
文献[97]	使用超尺度块作为核心结构构建超尺度目标检测框架
文献[99]	利用Transformer模型的预测主体提升多尺度预测能力
文献[100]	基于圆形平滑标签设计损失函数	针对遥感密集目标的改进

数据集	发布者及内容描述	目标类别数/个	图像数/幅
TAS ^[101]	由Stanford大学发布的车辆目标数据集	1	30
OIRDS ^[102]	由Raytheon公司发布的车辆目标数据集	5	900
SZTAKI ^[103]	由Mta Sztaki发布的旋转建筑物目标数据集	1	9
UCAS-AOD ^[104]	由中国科学院发布的车辆和飞机目标数据集，以及背景负样本	2	976
NWPU VHR-10 ^[105]	由西北工业大学发布的飞机、舰船、油罐、棒球场、网球场、篮球场等目标数据集	10	1 510
VEDAI ^[106]	由Caen大学发布的车辆目标数据集	9	1 210
HRSC2016 ^[107]	由西北工业大学发布的舰船目标数据集	1	1 061
DLR3k ^[108]	由German Aerospace Center发布的车辆目标数据集	7	20
RSOD ^[31]	由武汉大学发布的飞机、油箱、运动场和立交桥目标数据集	4	976
TGRS-HRRSD ^[109]	由中国科学院发布的舰船、桥梁、田径场、油罐、篮球场、网球场等目标数据集	13	21 761
LEVIR ^[110]	由北京航空航天大学发布的飞机、舰船、油罐目标数据集	3	22 000
ITCVD ^[111]	由Twente大学发布的车辆目标数据集	1	135
DIOR ^[15]	由西北工业大学发布的飞机、机场、篮球场、桥梁、烟囱、水坝等目标数据集	20	23 463
DOTA ^[112]	由武汉大学发布的舰船、游泳池、田径场、港口、直升机、足球场等目标数据集	16	2 806
FAIR1M ^[113]	由中国科学院等发布的飞机、舰船、车辆、球场、道路等5种大类别、37个细粒度类别的数据集，是目前全球规模最大的光学遥感图像细粒度目标检测识别数据集	37	15 000

数据集	算法	主干网络	改进功能				mAP
数据集	算法	主干网络	目标旋转	微小目标	多尺度	目标密集	mAP
	GWD^[83]	ResNet152	√			√	80.23
	HSP^[21]	ResNet101	√		√		78.01
	NLFE^[27]	ResNet50	√				77.46
	DCL^[82]	ResNet152	√	√			77.37
	R³Det^[56]	ResNet152	√			√	76.47
	CLS^[81]	ResNet152	√			√	76.24
	APERO^[42]	ResNet101	√	√			75.75
	FFA^[22]	ResNet101	√		√		75.70
	SCFPN^[23]	ResNet101			√		75.22
DOTA数据集	Gliding vertex^[78]	ResNet101	√				75.02
	RSDet^[77]	ResNet152	√			√	74.10
	DR-Net^[84]	CenterNet	√				73.23
	GLS-Net^[94]	ResNet101	√		√		72.96
	IoU-Adaptive^[38]	ResNet101		√	√		72.72
	FMSSD^[75]	VGG16		√	√		72.43
	CAD-Net^[25]	ResNet101	√				69.60
	RoITransformer^[41]	ResNet101	√		√		69.56
	TEANS^[44]	ResNet101		√			66.01
	AFF-SSD^[67]	ResNet50		√			52.60
	HyNet^[97]	ResNet50		√	√		98.52
	SCRDet^[79]	ResNet101	√	√		√	91.75
	MS-OPN AODN^[38]	GoogleNet		√	√	√	91.52
	CAD-Net^[25]	ResNet101	√				91.50
	FMSSD^[75]	VGG16		√	√		90.40
	AF-SSD^[69]	ShuffleNetV2		√			88.70
NWPU VHR-10数据集	RICA^[32]	ZFNet	√				87.12
	Deformable Faster RCNN^[35]	ResNet50					84.40
	Sig-NMS^[33]	VGG16		√			82.90
	PSB^[30]	ResNet101					81.20
	HSF-Net^[26]	VGG16		√	√		81.15
	Deformable R-FCN^[43]	ResNet101			√		79.10
	R-P-Faster R-CNN^[29]	VGG16			√		76.50
	DFSSD^[68]	VGG16		√			65.35

Research progress of deep learning-based object detection of optical remote sensing image

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