Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (8): 29025-029025.doi: 10.7527/S1000-6893.2023.29025
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Qichang ZHAO1,2, Yiquan WU1(), Yubin YUAN1
Received:
2023-05-22
Revised:
2023-06-21
Accepted:
2023-07-11
Online:
2024-04-25
Published:
2023-07-21
Contact:
Yiquan WU
E-mail:nuaaimage@163.com
Supported by:
CLC Number:
Qichang ZHAO, Yiquan WU, Yubin YUAN. Progress of ship detection and recognition methods in optical remote sensing images[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(8): 29025-029025.
Table 1
Main parameters of typical high resolution optical remote sensing satellites at home and abroad
卫星名称 | 轨道 | 波段/nm | 分辨率/m | 幅宽/km |
---|---|---|---|---|
WorldView-1 | 高度:496 km 太阳同步轨道 | 全色:400~900 | 0.5 | 17.7 |
WorldView-2 | 高度:771 km 太阳同步轨道 | 全色:450~800 | 0.46 | 16.4 |
WorldView-3 | 高度:617 km 太阳同步轨道 | 全色:450~800 | 0.31 | 13.1 |
WorldView-4 | 高度:617 km 太阳同步轨道 | 多光谱: 蓝:450~510 绿:510~580 红:655~690 近红外:780~920 | 全色:0.31 多光谱:1.24 | 13.1 |
GeoEye-1 | 高度:681 km 太阳同步轨道 | 全色:450~900 蓝:450~510 绿:520~580 红:655~690 近红外:780~920 | 全色:0.41 多光谱:1.65 | 15.2 |
Planet卫星星座 | 高度: 国际空间站轨道:400 km 太阳同步轨道:475 km | 国际空间站轨道 太阳同步轨道蓝:455~515 蓝:455~515绿:500~590 绿:500~590红:590~670 红:590~670近红外:780~860 近红外:780~860 | 400 km轨道:3 475 km轨道:3.7 | 24.6 |
SkySat卫星系列 | 高度:630 km 太阳同步轨道 | 蓝:440~510 绿:520~590 红:630~685 近红外:760~850 | 5 | 77 |
Pleiades星座 | 高度:694 km 太阳同步轨道 | 全色:470~830 蓝:430~550 绿:500~620 红:590~710 近红外:740~940 | 全色:0.5 多光谱:2 | 20 |
卫星名称 | 轨道 | 波段/nm | 分辨率/m | 幅宽/km |
---|---|---|---|---|
SPOT-6/7 | 高度:695 km 太阳同步轨道 | 全色:450~750 蓝:450~520 绿:530~600 红:620~690 近红外:760~890 | 全色:1.5 多光谱:6 | 60 |
DEIMOS-2 | 高度:620 km 太阳同步轨道 | 全色:560~900 蓝:466~525 绿:532~599 红:640~697 近红外:770~892 | 全色:0.75 多光谱:3 | 20 |
北京二号 | 高度:651 km 太阳同步轨道 | 全色:450~650 蓝:440~510 绿:510~590 红:600~670 近红外:760~910 | 全色:0.8 多光谱:3.2 | 24 |
高景一号 | 高度:530 km 太阳同步轨道 | 全色:450~890 蓝:450~520 绿:520~590 红:630~690 近红外:770~890 | 全色:0.5 多光谱:2 | 12 |
高分一号 | 高度:645 km 太阳同步轨道 | 全色:450~900 | 全色:2 多光谱:8 | 60 |
蓝:450~520 | ||||
绿:520~590 | ||||
红:630~690 | ||||
近红外:770~890 | ||||
高分二号 | 高度:631 km 太阳同步轨道 | 全色:450~900 | 全色:1 多光谱:4 | 45 |
蓝:450~520 | ||||
绿:520~590 | ||||
红:630~690 | ||||
近红外:770~890 | ||||
高分六号 | 高度:644.5 km 太阳同步轨道 | 全色:450~900 | 全色:2 多光谱:8 | 90 |
蓝:450~520 | ||||
绿:520~600 | ||||
红:630~690 | ||||
近红外:760~900 | ||||
高分七号 | 高度:500 km 太阳同步轨道 | 全色:450~900 | 全色:前视0.65、后视0.8 多光谱:后视2.6 | 20 |
蓝:450~520 | ||||
绿:520~600 | ||||
红:630~690 | ||||
近红外:770~890 |
Table 2
Comparison of advantages and disadvantages and main application directions of three types of image defogging algorithms
类别 | 子类 | 代表算法 | 优点 | 局限性 | 主要应用 |
---|---|---|---|---|---|
基于图像增强的方法 | 对比度增强 | 直方图均衡化 | 图像还原前后对比度明显,算法复杂度较低 | 易产生噪声,对团雾增强效果较差 | 颜色较暗的薄雾 |
小波变换 | 较好增强图像细节 | 不能解决图像过亮过暗或光照不均情况 | 红外图像、医学图像 | ||
色彩恒常性 | Retinex算法 | 增强后视觉效果较好,去雾彻底,能保持图像原有亮度 | 算法复杂度高,对光晕有一定抑制作用但易出现颜色失真 | 航拍俯视图像 | |
白平衡算法 | 减少色偏图像,还原效果比较显著 | 运行速度慢,若颜色有一大片单一颜色导致炎症色差 | 色彩简单背景下图像复原 |
Table 4
Comparison of traditional ship detection methods in ocean background and land-sea background
分类 | 方法 | 优点 | 不足 |
---|---|---|---|
海洋背景下舰船目标候选区域提取 | 灰度信息方法 | 适用于水面平静、水域较暗和纹理均匀的图像,计算较简单 | 复杂海况,提取效果不好 |
模板匹配法 | 适用于海面平静且舰船目标与海面背景对比强烈的场合 | 复杂背景下边缘定位不准确 | |
分形模型和模糊集理论结合法 | 有效地利用了目标和背景之间的差异特性,可以有效解决数据不完备、不清晰问题 | 不能兼顾检测性能和耗时 | |
海陆背景下舰船目标候选区域提取 | 视觉感知原理法 | 较好地模拟了人类大脑视觉感知的特性 | 难以分析和提取目标的显著特征及显著图 |
港口先验知识法 | 能很好地解决靠岸舰船目标检测问题 | 较难获取精确的匹配模板 | |
图像分析法 | 不依赖港口的先验信息 | 舰船目标的显著性特征选取困难 |
Table 5
Comparison of typical deep learning target detection algorithms
分类 | 方法 | 优点 | 不足 | 文献 | 贡献 | 数据集 | mAP/% |
---|---|---|---|---|---|---|---|
两阶段算法 | R-CNN | CNN首次用于目标检测 | 训练繁琐,占资源,速度慢 | [ | 采用Soft-NMS替换 R-CNN算法中的 NMS | 作者自建 | 51.17 |
Fast R-CNN | 提出ROI层 | 算法耗时,无法实时 | [ | 通过Fast R-CNN框架实现了对舰船目标的检测 | 作者自建 | 83.79 | |
Faster R-CNN | 引入RPN,精度与速度提高 | 计算复杂,无法实时 | [ | RPN网络部分加入了K-means聚类算法,速度更快 | NWPU VHR-10[ | 95.18 | |
R-FCN | 共享网络,速度与性能提升 | 速度有提升,但仍不能实时 | [ | 对特征提取网络进行混合尺度卷积核处理,使特征提取网络能够抑制相干斑噪声 | 作者自建 | 97.37 | |
一阶段算法 | YOLOv1 | 速度较快 | 小的、靠近的目标检测较差 | [ | 将YOLO引入船舶检测 | 作者自建 | 57.3 |
YOLOv2 | 分类检测联合训练,速度更快 | 输入尺寸固定,小目标精度差 | [ | 结合SVM,实现复杂背景下船舶分类 | 作者自建 | 80.5 | |
YOLOv3 | 多尺度预测,小目标检测好 | 目标位置精准性不高,召回率低 | [ | 设计了一种多尺度和自适应特征处理模块 | SeaShips [ | 91.47 | |
YOLOv4 | 兼顾了检测精度和速度 | 检测精度有待进一步提高 | [ | 采用Softer-NMS对非极大值抑制算法进行优化,提升模型对密集船舶的检测能力和定位精度 | SeaShips+作者扩充 | 96.78 | |
YOLOv5 | 模型尺寸小,部署成本低,灵活性高,检测速度高 | 检测精度可以进一步提高 | [ | 引入Mixup数据增强方法,采用Focal loss损失函数,用K-means聚类算法对数据集重新聚类 | SeaShips | 98.6 | |
YOLOv6 | 相对同类规模的算法,在检测精度和速度之间取得最佳权衡 | 性能有待提高 | / | / | / | ||
YOLOX | 算法精度与速度进一步提升 | 大尺寸样本训练慢 | [ | 引入了CA位置注意力模块,将CIoU损失和Focal loss损失引入到模型优化训练阶段 | HRSC2016[ | 94.37 | |
YOLOv7 | 引入锚框机制,进一步提高了目标回归率 | 增加了训练成本 | [ | 增强了复杂背景下对舰船关键特征的提取能力 | HRSID [ | 95.37 | |
SSD | 适应多尺度目标训练与检测 | 调试过程非常依赖经验,小、近目标精度差 | [ | 结合特征融合思想,提出基于特征融合的改进算法 | 作者自建 | 81.7 | |
SSD | 比SSD精度高 | 模型复杂,速度较慢 | 作者自建 | 82.6 | |||
FSSD | 性能上比SSD有很大的提高 | 速度略有下降 | / | / | / |
Table 6
Transformer-based object detection algorithm
类型 | 模型 | 优点 | 缺点 | 适用场景 | 数据集 | AP/% |
---|---|---|---|---|---|---|
Transformer neck | DETR | 网络结构简单,端到端训练,简化检测流程,消除NMS和锚框生成等手工组件需求 | 训练时间太长,小目标检测效果差 | 目标检测 全景分割 | COCO | 42.0 |
Deformable DETR | 加快收敛速度、提高小目标检测精度,计算和内存效率高 | 无序的内存访问,检测速度有所下降 | 目标检测 | COCO | 46.9 | |
TSP-FCOS | 采用encoder-only的DETR,网络结构简单,收敛速度加快,小目标精度提升 | 大目标精度降低 | 目标检测 | COCO | 43.1 | |
TSP-RCNN | 网络结构简单,收敛速度加快,小目标精度提升 | 执行边框细化操作会增加更多的计算资源 | 目标检测 | COCO | 45.0 | |
ACT | 缓解了目标查询的冗余现象,降低了计算量 | 小目标检测效果不好 | 目标检测 | COCO | 43.1 | |
UP-DETR | 比DETR精度高、收敛速度快 | 小目标检测效果依然不好 | 目标检测 全景分割 | COCO VOC | 42.8 56.1 | |
Transformer backbone | FPT | 高效的特征交互算法,实现特征信息跨空间和尺度交互,即插即用,整体思路新颖 | 计算量和参数量较大 | 目标检测 实例分割 语义分割 | COCO | 42.6 |
Swin Transformer | 在各类基准上精度很高,远超于已有模型 | 拥有线性计算复杂度 | 图像分类 目标检测 语义分割 | COCO | 58.7 | |
Swin Transformer V2 | 大型视觉模型,模型训练稳定,实现不同分辨率之间模型的有效转换 | 参数量大 | COCO | 63.1 |
Table 11
Detection results of different models in SeaShips dataset
模型 | mAP |
---|---|
Fast(VGG16) | 0.710 3 |
Faster(ZF) | 0.891 6 |
Faster(VGG16) | 0.901 2 |
Faster(ResNet18) | 0.906 3 |
Faster(ResNet50) | 0.916 5 |
Fast(VGG16) | 0.710 3 |
Faster(ZF) | 0.891 6 |
Faster(VGG16) | 0.901 2 |
Faster(ResNet18) | 0.906 3 |
Faster(ResNet50) | 0.916 5 |
Faster(ResNet101) | 0.924 0 |
SSD 300(MobileNet) | 0.776 6 |
SSD 608(MobileNet) | 0.795 0 |
SSD 300(VGG16) | 0.793 7 |
SSD 608(VGG16) | 0.867 3 |
YOLO v2 random=0 | 0.775 1 |
YOLO v2 random=1 | 0.790 6 |
Table 12
Detection results of different models in NWPU VHR-10 dataset
模型 | mAP |
---|---|
Transferred CNN | 0.597 |
Newly Trained CNN | 0.618 |
RICNN without Fine-Tuning | 0.710 |
RICNN with Fine-Tuning | 0.726 |
R-P-Faster R-CNN(Double) R-CNN(ZF) | 0.702 |
R-P-Faster R-CNN(Double) R-CNN(VGG16) | 0.743 |
R-P-Faster R-CNN(Single) R-CNN(VGG16) | 0.765 |
Table 17
Detection results of different models in DIOR dataset
模型 | 骨干网络 | mAP |
---|---|---|
R-CNN | VGG16 | 0.377 |
RICNN | VGG16 | 0.442 |
RICAOD | VGG16 | 0.509 |
RIFD-CNN | VGG16 | 0.561 |
Faster R-CNN | VGG16 | 0.541 |
SSD | VGG16 | 0.586 |
YOLOv3 | Darknet-50 | 0.571 |
Faster RCNN with FPN | ResNet-50 | 0.631 |
Mask-RCNN with FPN | ResNet-101 | 0.652 |
RetinaNet | ResNet-101 | 0.657 |
PANet | ResNet-101 | 0.661 |
CornerNet | Hourglass-104 | 0.649 |
Table 22
Optical remote sensing image datasets containing vessels
数据集 | 年份 | 单位 | 图像总数 | 船舶图像数量 | 备注 | 下载链接 |
---|---|---|---|---|---|---|
SeaShips | 2018 | 武汉大学 | 31 455 | 10 800 | 66种目标 | http:∥www.lmars.whu.edu.cn/prof_web/shaozhenfeng/datasets/SeaShips%287000%29.zip |
NWPU VHR-10 | 2014 | 西北工业大学 | 800 | 57 | 共有10类遥感目标 | https:∥pan.baidu.com/s/1hqwzXeG#list/path=%2F |
NWPU-RESISC45 | 2016 | 西北工业大学 | 31 500 | 700 | 45类遥感目标,图像尺寸为256×256 | https:∥pan.baidu.com/s/1mifR6tU#list/path=%2F |
HRSC2016 | 2016 | 中国科学院自动化研究所 | 1 070 | 1 070 | 用于船舶检测与分类,2 976个、超过25类船舶目标 | https:∥pan.baidu.com/share/init?surl=Sz2aohknDVCYrnXcnPQuaQ |
DOTA | 2017 | 武汉大学 | 2 806 | 15类遥感目标,包含数万个船舶目标 | https:∥captain-whu.github.io/DOTA/index.html | |
DIOR | 2018 | 西北工业大学 | 23 463 | 1 200 | 共有20类遥感目标 | http:∥www.escience.cn/people/gongcheng/DIOR.html |
MASATI | 2019 | 阿里坎特大学 | 7 389 | 4 157 | 7类目标,可用于船舶检测与分类 | https:∥www.iuii.ua.es/datasets/masati/ |
HRRSD | 2019 | 中国科学院大学 | 21 761 | 1 898 | 13类目标,图像分辨率0.15~1.2 m | https:∥pan.baidu.com/s/11OhYOZ2SrBc_lKY5LR_8gw |
iSAID | 2019 | 武汉大学 | 2 806 | 15类目标,图像像素400~15 000 | https:∥captain-whu.github.io/iSAID/dataset.html | |
xView | 2018 | DIUx and NGA | 22 000+ | 10 000 | 60类目标,图像像素2 500~5 000 | https:∥pan.baidu.com/s/10zQb06R8KoBLswmfS2jTgw |
LEVIR | 2018 | 北京航空航天大学 | 22 000 | 3类目标,800×600,3 025艘船 | https:∥pan.baidu.com/s/1hVx74Q4waNRKsC6yEhrHoQ |
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