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基于支持向量机的长波红外目标分类识别算法

王周春 崔文楠 张涛

王周春, 崔文楠, 张涛. 基于支持向量机的长波红外目标分类识别算法[J]. 红外技术, 2021, 43(2): 153-161.
引用本文: 王周春, 崔文楠, 张涛. 基于支持向量机的长波红外目标分类识别算法[J]. 红外技术, 2021, 43(2): 153-161.
WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.
Citation: WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.

基于支持向量机的长波红外目标分类识别算法

详细信息
    作者简介:

    王周春(1989-),男,硕士研究生,研究方向:红外图像处理,目标识别,机器学习。E-mail:wangzhch@shanghaitech.edu.cn

    通讯作者:

    张涛(1966-),男,博士,二级研究员,研究方向:光电技术与系统,空间科学仪器,目标光学探测与数字仿真。E-mail:haozzh@sina.com

  • 中图分类号: TN219

Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine

  • 摘要: 红外图像的分辨率低和色彩单一,但由于红外设备的全天候工作特点,因而在某些场景具有重要作用。本文采用一种基于支持向量机(support vector machine, SVM)的长波红外目标图像分类识别的算法,在一幅图像中,将算法提取的边缘特征和纹理特征作为目标的识别特征,输入到支持向量机,最后输出目标的类别。在实验中,设计方向梯度直方图+灰度共生矩阵+支持向量机的组合算法模型,采集8种人物目标场景图像进行训练和测试,实验结果显示:相同或者不相同人物目标,穿着不同服饰,算法模型的分类识别正确率较高。因此,在安防监控、工业检测、军事目标识别等运用领域,此组合算法模型可以满足需要,在红外目标识别领域具有一定的优越性。
  • 图  1  图像边缘特征

    Figure  1.  Image edge features

    图  2  算法模型工作流程

    Figure  2.  Algorithm model work

    图  3  线性SVM

    Figure  3.  Linear SVM

    图  4  非线性模型SVM

    Figure  4.  Non-linear model SVM

    图  5  HOG选择

    Figure  5.  HOG selection

    图  6  原始图像以及对应的HOG

    Figure  6.  Original image and corresponding HOG

    图  7  空间位置关系

    Figure  7.  Spatial position relation

    图  8  Class A和Class B的灰度直方图分布

    Figure  8.  Grayscale histogram distribution of Class A and Class B

    图  9  八种目标场景图像

    Figure  9.  Eight kinds target scene images

    图  10  模型分类结果

    Figure  10.  Model classification results

    表  1  混淆矩阵

    Table  1.   Confusion matrix

    Confusion matrix Predictivevalue
    Class 1 Class 2 Class 3 …. Class 8
    Real value Class 1 T11 F12 F13 F18
    Class 2 F21 T22 F23 F28
    Class 3 F31 F32 T33 F38
    …. ..
    Class 8 F81 F82 F83 T88
    下载: 导出CSV

    表  2  图像场景代表意义

    Table  2.   Image scene representative meaning

    Class Representative meaning
    Class A Target U wears camouflage clothes indoors at night
    Class B Target U wears ordinary clothes indoors at night
    Class C Target U wears camouflage clothes outdoors during the day
    Class D Target U wears camouflage clothes outdoors at night
    Class E Target V wears ordinary clothes outdoors at night
    Class F Target W wears ordinary clothes indoors at night
    Class G Target X wears ordinary clothes outdoors during the day
    Class H Target Y wears ordinary clothes outdoors during the day
    下载: 导出CSV

    表  3  模型分类结果

    Table  3.   Model classification results

    Confusion matrix Predictive value
    Class A Class B Class C Class D Class E Class F Class G Class G
    Real value Class A 111 0 0 0 0 0 0 0
    Class B 1 110 0 0 0 0 0 0
    Class C 0 0 43 1 2 0 1 0
    Class D 0 0 3 44 0 0 0 0
    Class E 0 0 2 2 31 0 0 0
    Class F 0 0 1 2 2 16 0 0
    Class G 0 0 7 3 6 0 4 0
    Class H 0 0 2 2 1 1 0 12
    下载: 导出CSV
  • [1] 曹凤杰. 红外图像人脸识别方法研究[D]. 西安: 西安电子科技大学, 2010.

    CAO Fengjie. Research on Infrared Image Face Recognition Method[D]. Xi'an: Xidian University, 2010.
    [2] Der S Z, Chellappa R. Probe-based automatic target recognition in infrared imagery[J]. IEEE Transactions on Image Processing, 1997, 6(1): 92-102. doi:  10.1109/83.552099
    [3] 姜锦锋. 红外图像的目标检测、识别与跟踪技术研究[D]. 西安: 西北工业大学, 2004.

    JIANG Jinfeng. Research on Target Detection, Recognition and Tracking Technology of Infrared Image[D]. Xi'an: Northwestern Polytechnical University, 2004.
    [4] 郭济民. 基于深度神经网络的物体识别方法研究及实现[D]. 成都: 电子科技大学, 2018.

    GUO Jimin. Research and Implementation of Object Recognition Method Based on Deep Neural Network[D]. Chengdu: University of Electronic Science and Technology, 2018.
    [5] Abdulkadir Eryildirim, Ibrahim Onaran. Pulse Doppler radar target recognition using a two-stage SVM procedure[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 1450-1457. doi:  10.1109/TAES.2011.5751269
    [6] 李小迷. 葡萄糖药液中异物目标视觉检测与识别方法研究[D]. 长沙: 湖南大学, 2010.

    LI Xiaomi. Research on Visual Inspection and Recognition Method of Foreign Objects in Glucose Liquid[D]. Changsha: Hunan University, 2010.
    [7] 王朔琛, 汪西莉, 马君亮. 基于均值漂移的半监督支持向量机图像分类[J]. 计算机应用, 2014, 34(8): 2399-2403. doi:  10.3969/j.issn.1001-3695.2014.08.038

    WANG Shuochen, WANG Xili, MA Junliang. Semi-supervised support vector machine image classification based on mean shift[J]. Journal of Computer Applications, 2014, 34(8): 2399-2403. doi:  10.3969/j.issn.1001-3695.2014.08.038
    [8] 丁方静. 室内监控中移动检测与跟踪算法的改进与实现[D]. 南京: 东南大学, 2017.

    DING Fangjing. Improvement and Implementation of Moving Detection and Tracking Algorithm in Indoor Monitoring[D]. Nanjing: Southeast University, 2017
    [9] 卞海曼. 基于卷积神经网络的行人检测[D]. 合肥: 合肥工业大学, 2017.

    BIAN Haiman. Pedestrian Detection Based on Convolutional Neural Network[D]. Hefei: Hefei University of Technology, 2017.
    [10] Navneet Dalal, Bill Triggs. Histograms of oriented gradients for human detection[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
    [11] Minho J, Y Hee Yong, C Hsiao-Hwa. Intelligent RFID tag detection using support vector machine[J]. IEEE Transactions on Wireless Communications, 2009, 8(10): 5050-5059. doi:  10.1109/TWC.2009.071198
    [12] WANG R P, CHEN J, SHAN S G, et al. Enhancing training set for face detection based on SVM[J]. Journal of Software, 2009, 19(11): 2921-2931. doi:  10.3724/SP.J.1001.2008.02921
    [13] 尤倩. 基于SVM的脱机手写体数字识别的研究与应用[D]. 济南: 山东师范大学, 2014.

    YOU Qian. Research and Application of Offline Handwritten Digit Recognition Based on SVM[D]. Jinan: Shandong Normal University, 2014.
    [14] 张小琴, 赵池航, 沙月进. 基于HOG特征及支持向量机的车辆品牌识别方法[J]. 东南大学学报: 自然科学版, 2013, 43(2): 410-413. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX2013S2041.htm

    ZHANG Xiaoqin, ZHAO Chihang, SHA Yuejin. Vehicle brand recognition method based on HOG features and support vector machine[J]. Journal of Southeast University: Natural Science Edition, 2013, 43(2): 410-413. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX2013S2041.htm
    [15] LI Weixing, SU Haijun, PAN Feng, et al. A fast pedestrian detection via modified HOG feature[C]//Proceedings of the 34th Chinese Control Conference of IEEE, 2015: 3870-3873.
    [16] 曾雪. 基于旋转不变梯度方向直方图的航拍图像目标检测[D]. 南京: 东南大学, 2017.

    ZENG Xue. Object Detection Based on Rotation Invariant Histogram of Oriented Gradient in Aerial Image[D]. Nanjing: Southeast University, 2017.
    [17] Alex Omid-Zohoor, Christopher Young, David Ta, et al. Toward always-on mobile object detection: energy versus performance tradeoffs for embedded HOG feature extraction[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(5): 1102-1115. doi:  10.1109/TCSVT.2017.2653187
    [18] 段嘉欣. 基于梯度下降的时变PID算法[J]. 中国新通信, 2019, 21(14): 223-226. https://www.cnki.com.cn/Article/CJFDTOTAL-TXWL201914177.htm

    DUAN Jiaxin. Time-varying PID algorithm based on gradient descent[J]. China New Telecommunications, 2019, 21(14): 223-226. https://www.cnki.com.cn/Article/CJFDTOTAL-TXWL201914177.htm
    [19] CHEN Pei-Yin, HUANG Chien-Chuan, Lien Chih-Yuan, et al. An efficient hardware implementation of HOG feature extraction for human detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 656-662. doi:  10.1109/TITS.2013.2284666
    [20] HE Jiayuan, ZHU Xiangyang. Combining improved gray-level co-occurrence matrix with high density grid for myoelectric control robustness to electrode shift[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(9): 1539-1548. doi:  10.1109/TNSRE.2016.2644264
    [21] 叶鹏, 王永芳, 夏雨蒙, 等. 一种融合深度基于灰度共生矩阵的感知模型[J]. 计算机科学, 2019, 46(3): 92-96. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903012.htm

    YE Peng, WANG Yongfang, XIA Yumeng, et al. Perceptual model based on GLCM combined with depth[J]. Computer Science, 2019, 46(3): 92-96. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903012.htm
    [22] 王红, 武继刚, 张铮. 基于二维MB_LBP特征的人脸识别[J]. 计算机工程与应用, 2015, 51(10): 191-194. doi:  10.3778/j.issn.1002-8331.1305-0396

    WANG Hong, WU Jigang, ZHANG Zheng. Face recognition based on 2-dimensional MB-LBP characteristics[J]. Computer Engineering and Applications, 2015, 51(10): 191-194. doi:  10.3778/j.issn.1002-8331.1305-0396
    [23] Marceau D J, Howarth P J, Dubois J M, et al. Evaluation of the grey -level co-occurrence matrix method for land-cover classification using SPOT imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(4): 513-519. doi:  10.1109/TGRS.1990.572937
    [24] Simon D, Simon D L. Analytic confusion matrix bounds for fault detection and isolation using a sum-of-squared-residuals approach[J]. IEEE Transactions on Reliability, 2010, 59(2): 287-296. doi:  10.1109/TR.2010.2046772
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出版历程
  • 收稿日期:  2020-01-06
  • 修回日期:  2020-01-31
  • 刊出日期:  2021-02-20

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