光学技术, 2017, 43 (5): 445, 网络出版: 2017-11-07  

二值化特征在快速舰船目标预选中的应用

Improved binarized normed gradients for fast ship target pre-detection
作者单位
南京航空航天大学 航天学院, 南京 210016
摘要
为了快速准确地自动提取和识别海面舰船疑似目标, 为舰船目标精检测提供可信的数据基础, 采用了二值化特征进行舰船目标粗检测, 并根据舰船窄而长的几何特征提出了改进算法, 按照舰船目标不同的方向分别进行模板训练。实验表明, 二值化特征可以有效地提取疑似舰船目标, 并且改进算法可以在提取窗口数相同时, 提高查全率, 更利于进一步的精检测。
Abstract
For accurate and efficient detection and recognition of suspected ship target, reliable data base is provided for fine detection. The binarized feature for the coarse detection of ship target is adopt. An improved algorithm is proposed based on the narrow and long geometric feature of ship. Different templates are trained on the basis of the direction of ships. Experiments demonstrate that the binarized feature performs well in the extraction of candidate ship windows, and the improved algorithm can obtain a higher recall rate than original binarized feature when extracting the same amount of windows, which is helpful for the more efficient and accurate fine detection.
参考文献

[1] 王彦情, 马雷, 田原.光学遥感图像舰船目标检测与识别综述[J].自动化学报, 2011, 37(9): 1029-1039.

    WANG Yanqing, MA Lei, TIAN Yuan. State-of-the-art of ship detection and recognition in optical remotely sensed imagery[J]. Journal of Automatica Sinica, 2011, 37(9): 1029-1039.

[2] 蒋李兵. 基于高分辨光学遥感图像的舰船目标检测方法研究[D].长沙: 国防科学技术大学, 2006.

    JIANG Libing. Research on the ship target detection in high spatial resolution optical remote sensing image[D]. Changsha: National University of Defense Technology, 2006.

[3] 周志远. 基于光学遥感图像的海面舰船目标识别技术研究[D]. 北京: 中国科学院研究生院, 2012.

    ZHOU Zhiyuan. Research of the ship target recognition technology based on optical remote sensing image[D]. Beijing:Graduate University of Chinese Academy of Sciences,2012.

[4] 唐沐恩, 林挺强, 文贡坚.遥感图像中舰船检测方法综述[J].计算机应用研究, 2011, 28(1): 29-36.

    TANG Muen, LIN Tingqiang, WEN Gongjian. Overview of ship detection methods in remote sensing image[J]. Application Research of Computers, 2011, 28(1):29-36.

[5] 高立宁, 毕福昆, 龙腾,等.一种光学遥感图像海面舰船检测算法[J].北京: 清华大学学报, 2011, 51(1): 105-110.

    GAO Lining, BI Fukun, LONG Teng, et al. Ship detection algorithm for optical remote sensing images[J]. Journal of Tsinghua University, 2011, 51(1):105-110.

[6] 郭明玮.基于视觉记忆的目标检测算法: 一个特征学习与特征联想的过程[D].合肥: 中国科学技术大学, 2014.

    GUO Mingyi. Object detection algorithm based on visual memory: a feature learning and feature imagination process[D]. Hefei:University of Science and Technology of China,2014.

[7] 郭明玮, 赵宇宙, 项俊平,等. 基于支持向量机的目标检测算法综述[J].控制与决策, 2014, 29(2): 193-200.

    GUO Mingyi, ZHAO Yuzhou, XIANG Junping, et al. Review of object detection methods based on SVM[J]. Control and Decision, , 2014, 29(2):193-200.

[8] 裴巧娜. 基于光流法的运动目标检测与跟踪技术[D]. 北京: 北方工业大学, 2009.

    PEI Qiaona. Moving objects detection and tracking techniques based optical flow[D]. Beijing: North China University of Technology,2009.

[9] OLSHAUSEN B A, FIELD D J. Sparse coding with an over complete basis set: A strategy employed by V1[J]. Vision Research,1997,37(23):3311-3325.

[10] CHENG M M, ZHANG Z, LIN W Y, et al. BING: binarized normed gradients for objectness estimation at 300fps[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, Piscataway, NJ, USA: IEEE, 2014: 3286-3293.

[11] ALEXE B, DESELAERS T, FERRARI V. Measuring the objectness of image windows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(11).

[12] FORSYTH D A, MALIK J, FLECK M M, et al. Finding pictures of objects in large collections of images[C]∥European Conference on Computer Vision, Springer, Berlin, Heidelberg,1996.

[13] FAN R E,CHANG K W, HSIEH C J, et al. Liblinear: A library for large linear classification[J]. Journal of Machine Learning Research, 2008,(9):1871-1874.

[14] Zhang Z, WARRELL J, TORR P H S. Proposal generation for object detection using cascaded ranking svms[C]∥In CVPR, 2011 IEEE Conference on. IEEE, 2011:1497-1504.

[15] CARREIRA J, SMINCHISESCU C. Cpmc: Automatic object segmentation using constrained parametric min-cuts[J]. IEEE TPAMI, 2012, 34(7):1312-1328.

黄伟, 严小乐, 沈秋, 李再升, 董克松, 顾逸佳. 二值化特征在快速舰船目标预选中的应用[J]. 光学技术, 2017, 43(5): 445. HUANG Wei, YAN Xiaole, SHEN Qiu, LI Zaisheng, DONG Kesong, GU Yijia. Improved binarized normed gradients for fast ship target pre-detection[J]. Optical Technique, 2017, 43(5): 445.

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