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Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions


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Xu Fang. Research on key Technologies for Automatic Detection of Surface Objects in Visible light remote sensing images [D]. University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences), 2018.XuFangResearch on key Technologies for Automatic Detection of Surface Objects in Visible light remote sensing images [D]University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences)2018Search in Google Scholar

Zhang Zemiao, Huo Huan, Zhao Fengyu. Review of Object detection algorithms for deep convolutional neural networks [J]. Minicomputers, 2019, 40(09): 1825-1831.ZhangZemiaoHuoHuanZhaoFengyuReview of Object detection algorithms for deep convolutional neural networks [J]Minicomputers2019400918251831Search in Google Scholar

Yao Qunli, HU Xian, LEI Hong. Research Progress of Deep convolutional neural network in Object Detection [J]. Computer Engineering and Applications, 2016,54(17):1–9.YaoQunliHUXianLEIHongResearch Progress of Deep convolutional neural network in Object Detection [J]Computer Engineering and Applications2016541719Search in Google Scholar

Xu Junfeng, Zhang Baoming, Guo Haitao. A multi-feature fusion object-oriented multi-source remote sensing image change detection method [J]. Journal of surveying and mapping science and technology, 2015, 32(05):505–509.XuJunfengZhangBaomingGuoHaitaoA multi-feature fusion object-oriented multi-source remote sensing image change detection method [J]Journal of surveying and mapping science and technology20153205505509Search in Google Scholar

Sun Hao, Sun Xian, Wang Hongqi. Research on a high-resolution remote sensing image ship detection method [J]. Surveying and mapping science, 2013, 38(5):112–116.SunHaoSunXianWangHongqiResearch on a high-resolution remote sensing image ship detection method [J]Surveying and mapping science2013385112116Search in Google Scholar

Wang Tengfei. Research on high-resolution Remote Sensing Image Deep Learning Ship Detection Technology [D]. Harbin Institute of Technology, 2017.WangTengfeiResearch on high-resolution Remote Sensing Image Deep Learning Ship Detection Technology [D]Harbin Institute of Technology2017Search in Google Scholar

Wang Jinchuan, TAN Xicheng, WANG Zhaohai, ZHONG Yanfei, Dong Huaping, Zhou Songtao, Cheng Boyi. Research on remote sensing image Object recognition Method based on Faster R-CNN Deep Network [J]. Journal of Earth Information Science, 2016, 20(10):1500–1508.WangJinchuanTANXichengWANGZhaohaiZHONGYanfeiDongHuapingZhouSongtaoChengBoyiResearch on remote sensing image Object recognition Method based on Faster R-CNN Deep Network [J]Journal of Earth Information Science2016201015001508Search in Google Scholar

Yao Hongge, Wang Cheng, Yu Jun, Bai Xiaojun, Li Wei. Small Object ship identification in complex satellite images [J]. Journal of remote sensing, 2020, 24(02): 116-125.YaoHonggeWangChengYuJunBaiXiaojunLiWeiSmall Object ship identification in complex satellite images [J]Journal of remote sensing20202402116125Search in Google Scholar

Wu Jinliang, WANG Gang, Liang Shuo, Chen Jinyong, Gao Feng. Research on Ship Object Detection based on Mask R-CNN [J]. Radio Engineering, 2008, 48(11):947–952.WuJinliangWANGGangLiangShuoChenJinyongGaoFengResearch on Ship Object Detection based on Mask R-CNN [J]Radio Engineering20084811947952Search in Google Scholar

He Yubo, LIU Kun. Surface Significance Object Detection based on convolutional neural network [J/OL]. Computer Engineering and Application:1–10[2020-06-09].HeYuboLIUKunSurface Significance Object Detection based on convolutional neural network [J/OL]Computer Engineering and Application1102020-06-09Search in Google Scholar

Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. Computer Science, 2015(99):1.RenS QHeK MGirshickRFaster R-CNN: towards real-time object detection with region proposal networks [J]Computer Science2015991Search in Google Scholar

Zhang Y, Sohn K, Villegas R, et al Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction[C]. Computer Vision and Pattern Recognition. IEEE, 2015:249–258ZhangYSohnKVillegasRImproving object detection with deep convolutional networks via Bayesian optimization and structured prediction[C]Computer Vision and Pattern RecognitionIEEE201524925810.1109/CVPR.2015.7298621Search in Google Scholar

Girshick R. Fast R-CNN[C]. the IEEE International Conference on Computer Vision, December 7-13, 2015, Chile. New Jersey, IEEE Press, 2015:1440–1448.GirshickR.Fast R-CNN[C]the IEEE International Conference on Computer VisionDecember 7-13, 2015Chile. New JerseyIEEE Press20151440144810.1109/ICCV.2015.169Search in Google Scholar

Engineering; Study Data from Northeast Electric Power University Provide New Insights into Engineering (Fingerprint location algorithm based on K-means for spatial farthest access point in Wi-Fi environment)[J]. Journal of Engineering, 2020.EngineeringStudy Data from Northeast Electric Power University Provide New Insights into Engineering (Fingerprint location algorithm based on K-means for spatial farthest access point in Wi-Fi environment)[J]Journal of Engineering2020Search in Google Scholar

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