基于深度学习的海上遥感图像目标检测算法综述
Journal: Geological and Mineral Surveying and Mapping DOI: 10.12238/gmsm.v7i7.1890
Abstract
目标检测是海上遥感图像目标信息提取的关键环节,广泛应用于海上舰船检测、环境监测、岛礁变化等领域。近年来,随着深度学习的快速发展,基于深度学习的海上遥感图像目标检测准确性和实时性得到显著提升。本文首先介绍了基于深度学习的目标检测相对于传统的目标检测算法的优势,然后梳理了当前主流的目标检测算法发展演变和应用特点,接着介绍了当前目标检测的最新成果以及未来的发展趋势,结合海上遥感图像特点,归纳了一些对于海上遥感图像的目标检测研究成果。最后总结未来海上遥感图像面临的问题并提出相对应的解决思路。
Keywords
深度学习；目标检测；海上；遥感图像；算法
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[1] Ross G, Jeff D, Trevor D, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition.2014:580-587.
[2] Girshick R. Fast r-cnn[C]. Proceedings of the IEEE international conference on computer vision.2015:1440-1448.
[3] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J].Advances in neural information processing systems,2015,28.
[4] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition.2016:779-788.
[5] Wang C Y,Liao H Y M, Wu Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops.2020:390-391.
[6] Lin T Y,Dollár P, Girshick R,etal.Feature pyramid netwo rks for object detection[C]. Proceedings of the IEEE conferen ce on computer vision and pattern recognition.2017: 2117-2125.
[7] Liu W, Anguelov D,Erhan D,etal.Ssd:Single shot multibox detector[C].European conference on computer vision.Springer,Cham,2016:21-37.
[8] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J].Advances in neural information processing systems, 2017,30.
[9] Yang X, Sun H, Fu K, et al. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks [J].Remote Sensing,2018,10(1):132.
[10] Ming Q, Zhou Z, Miao L, et al. Dynamic anchor learning for arbitrary-oriented object detection[C]. Proceedings of the AAAI Conference on Artificial Intelligence.2021,35(3):2355-2363.
[11] 张玉莲.光学图像海面舰船目标智能检测与识别方法研究[D].中国科学院大学(中国科学院长春光学精密机械与物理研究所),2021.
[12] 孙乔.基于深度卷积神经网络的舰船目标识别技术研究[D].杭州电子科技大学,2019.
[13] 成倩,李佳,杜娟.基于YOLOv5的光学遥感图像舰船目标检测算法[J/OL].系统工程与电子技术:1-9[2022-11-28].
[14] 周旗开,张伟,李东锦,牛福.基于改进YOLOv5s的光学遥感图像舰船分类检测方法[J].激光与光电子学进展,2022,59(16):476-483.
[2] Girshick R. Fast r-cnn[C]. Proceedings of the IEEE international conference on computer vision.2015:1440-1448.
[3] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J].Advances in neural information processing systems,2015,28.
[4] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition.2016:779-788.
[5] Wang C Y,Liao H Y M, Wu Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops.2020:390-391.
[6] Lin T Y,Dollár P, Girshick R,etal.Feature pyramid netwo rks for object detection[C]. Proceedings of the IEEE conferen ce on computer vision and pattern recognition.2017: 2117-2125.
[7] Liu W, Anguelov D,Erhan D,etal.Ssd:Single shot multibox detector[C].European conference on computer vision.Springer,Cham,2016:21-37.
[8] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J].Advances in neural information processing systems, 2017,30.
[9] Yang X, Sun H, Fu K, et al. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks [J].Remote Sensing,2018,10(1):132.
[10] Ming Q, Zhou Z, Miao L, et al. Dynamic anchor learning for arbitrary-oriented object detection[C]. Proceedings of the AAAI Conference on Artificial Intelligence.2021,35(3):2355-2363.
[11] 张玉莲.光学图像海面舰船目标智能检测与识别方法研究[D].中国科学院大学(中国科学院长春光学精密机械与物理研究所),2021.
[12] 孙乔.基于深度卷积神经网络的舰船目标识别技术研究[D].杭州电子科技大学,2019.
[13] 成倩,李佳,杜娟.基于YOLOv5的光学遥感图像舰船目标检测算法[J/OL].系统工程与电子技术:1-9[2022-11-28].
[14] 周旗开,张伟,李东锦,牛福.基于改进YOLOv5s的光学遥感图像舰船分类检测方法[J].激光与光电子学进展,2022,59(16):476-483.
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