留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于热红外视频图像监测的海面溢油识别技术研究

王利锋 辛丽平 刘家硕 鞠莲

王利锋,辛丽平,刘家硕,等. 基于热红外视频图像监测的海面溢油识别技术研究[J]. 海洋学报,2022,44(5):148–160 doi: 10.12284/hyxb2022063
引用本文: 王利锋,辛丽平,刘家硕,等. 基于热红外视频图像监测的海面溢油识别技术研究[J]. 海洋学报,2022,44(5):148–160 doi: 10.12284/hyxb2022063
Wang Lifeng,Xin Liping,Liu Jiashuo, et al. Research on identification of marine oil spill based on thermal infrared video image monitoring[J]. Haiyang Xuebao,2022, 44(5):148–160 doi: 10.12284/hyxb2022063
Citation: Wang Lifeng,Xin Liping,Liu Jiashuo, et al. Research on identification of marine oil spill based on thermal infrared video image monitoring[J]. Haiyang Xuebao,2022, 44(5):148–160 doi: 10.12284/hyxb2022063

基于热红外视频图像监测的海面溢油识别技术研究

doi: 10.12284/hyxb2022063
基金项目: 山东省重点研发项目(2018GHY115025);国家自然科学基金 (201606141);山东省自然科学基金 (ZR2016FB04);中国博士后面上项目(2018M642611);南宁市科学研究与开发计划项目−重点研发类(20183045-2)。
详细信息
    作者简介:

    王利锋(1996-),男,河南省洛阳市人,主要研究方向为海上油膜检测与图像处理技术。E-mail: 463685634@qq.com

    通讯作者:

    辛丽平,女,副教授,主要研究方向为模式识别、图像处理技术和海上油膜检测。E-mail:lpxin@qut.edu.cn

  • 中图分类号: X55

Research on identification of marine oil spill based on thermal infrared video image monitoring

  • 摘要: 针对现有海面溢油检测技术难以在石油泄漏初期(尚未形成海面大规模油膜覆盖)及时发现油膜的难题,本文在前期基于热红外图像测算海面油膜面积方法研究的基础上,结合油泄漏至海面后油膜的扩散特征,提出了一种基于热红外视频图像监测油膜面积变化以及时识别海面溢油的方法。首先,基于单帧热红外图像处理算法提取海面前景区域(包含油膜区域与相似物干扰区域)并计算各区域所代表的实际物理面积。基于视频图像处理技术跟踪测算前景区域中各连通区域的实际物理面积变化情况,根据各连通区域的面积变化率识别前景区域中是否存在油膜,从而判断海面是否发生溢油。实验结果表明:所提出的方法能有效识别不同黏度的石油泄漏至海面形成的扩散油膜,在水面包含波浪与相似物干扰时也具有良好的识别精度。该方法适用于特定场景下(如码头、船舶等)的溢油事故的鉴别,能为溢油事故的及时发现和预警提供技术支持。
  • 图  1  水面油膜热红外视频采集示意

    Fig.  1  Schematic of thermal infrared video acquisition of water surface oil film

    图  2  油膜区域面积变化情况

    Fig.  2  Variation of the area of oil films

    图  3  水槽实验平静水面条件下的20W-50成品油单帧图像处理结果

    a. 原始热红外图像;b. 预处理结果;c. 前景区域分割结果;d. 面积计算结果

    Fig.  3  Image processing results of 20W-50 refined oil under calm water surface in tank experiment

    a. Original thermal infrared image; b. preprocessing result; c. segmentation result of foreground region; d. area calculation result

    图  4  含漂浮物干扰的水槽实验条件下的0W-20成品油单帧图像处理结果

    a. 原始热红外图像;b. 预处理结果;c. 前景区域分割结果;d. 面积计算结果

    Fig.  4  Image processing results of 0W-20 refined oil under water surface with floating objects in tank experiment

    a. Original thermal infrared image; b. preprocessing result; c. segmentation result of foreground region; d. area calculation result

    图  5  水槽实验平静水面条件下的20W-50成品油多帧图像处理结果

    a. 原始热红外图像;b. 阈值分割结果;c. 面积计算结果;d. 感兴趣的油膜区域标记结果

    Fig.  5  Multi-frame image processing results of 20W-50 refined oil under calm water surface in tank experiment

    a. Original thermal infrared images; b. threshold segmentation results; c. area calculation results; d. region of interest marked results

    图  6  水槽实验含波浪干扰水面条件下20W-50成品油多帧图像处理结果

    a. 原始热红外图像;b. 阈值分割结果;c. 面积计算结果;d. 感兴趣的油膜区域标记结果

    Fig.  6  Multi-frame image processing results of 20W-50 refined oil under water surface with waves in tank experiment

    a. Original thermal infrared images; b. threshold segmentation results; c. area calculation results; d. region of interest marked results

    图  7  水槽实验含波浪干扰水面条件下的0W-20成品油多帧图像处理结果

    a. 原始热红外图像;b. 阈值分割结果;c. 面积计算结果;d. 感兴趣的油膜区域标记结果

    Fig.  7  Multi-frame image processing results of 0W-20 refined oil under water surface with waves in tank experiment

    a. Original thermal infrared images; b. threshold segmentation results; c. area calculation results; d. region of interest marked results

    图  8  含漂浮物干扰的水槽实验条件下的20W-50成品油多帧图像处理结果

    a. 原始热红外图像;b. 阈值分割结果;c. 面积计算结果;d. 感兴趣的油膜区域标记结果

    Fig.  8  Multi-frame image processing results of 20W-50 refined oil under water surface with floating objects in tank experiment

    a. Original thermal infrared images; b. threshold segmentation results; c. area calculation results; d. region of interest marked results

    图  9  含漂浮物干扰的水槽实验条件下的0W-20成品油多帧图像处理结果

    a. 原始热红外图像;b. 阈值分割结果;c. 面积计算结果;d. 感兴趣的油膜区域标记结果

    Fig.  9  Multi-frame image processing results of 0W-20 refined oil under water surface with floating objects in tank experiment

    a. Original thermal infrared images; b. threshold segmentation results; c. area calculation results; d. region of interest marked results

    图  10  不同实验条件下的感兴趣的油膜区域面积变化情况

    Fig.  10  The changes of the area of region of interest under different experimental conditions

    图  11  含漂浮物干扰的水槽实验条件下的20W-50成品油图像帧间感兴趣的油膜区域面积变化率

    Fig.  11  The inter-frames region of interest area change rate of 20W-50 refined oil images under water surface with floating objects

    图  12  含漂浮物干扰的水槽实验条件下的0W-20成品油图像帧间感兴趣的油膜区域面积变化率

    Fig.  12  The inter-frames region of interest area change rate of 0W-20 refined oil images under water surface with floating objects

    图  13  水槽实验平静水面条件下20W-50成品油图像感兴趣的油膜区域提取结果对比

    a. 第11帧热红外图像;b. GMM;c. WEBS;d. SFS;e. 本文方法

    Fig.  13  Comparison of region of interest extraction results of 20W-50 refined oil image under calm water in tank experiment

    a. The 11th frame thermal infrared image; b. GMM; c. WEBS; d. SFS; e. proposed method

    图  14  水槽实验含波浪干扰水面条件下20W-50成品油图像感兴趣的油膜区域提取结果对比

    a. 第6帧热红外图像;b. GMM;c. WEBS;d. SFS;e. 本文方法

    Fig.  14  Comparison of region of interest extraction results of 20W-50 refined oil image under water surface with waves in tank experimsent

    a. The 6th frame thermal infrared image; b. GMM; c. WEBS; d. SFS; e. proposed method

    图  15  含漂浮物干扰的水槽实验条件下的20W-50成品油图像感兴趣的油膜区域提取结果对比

    a. 第16帧热红外图像;b. GMM;c. WEBS;d. SFS;e. 本文方法

    Fig.  15  Comparison of region of interest extraction results of 20W-50 refined oil image under water surface with floating objects in tank experiment

    a. The 16th frame thermal infrared image; b. GMM; c. WEBS; d. SFS; e. proposed method

    表  1  Fotric288参数规格

    Tab.  1  Specifications of the Fotric288

    参数规格
    工作波段8~14 μm
    NETD$\leqslant $30 mK
    视场角25°×19°
    镜头焦距12 mm
    分辨率640×480
    帧率30帧/s
    下载: 导出CSV

    表  2  实验条件

    Tab.  2  Experimental condition setting

    编号石油样品水面环境干扰类型
    20W-50成品油平静水面
    20W-50成品油含波浪干扰30 cm振幅的规则波
    20W-50成品油含漂浮物干扰10 cm×10 cm泡沫板
    0W-20成品油含波浪干扰30 cm振幅的规则波
    0W-20成品油含漂浮物干扰10 cm×10 cm泡沫板
    下载: 导出CSV

    表  3  不同方法的感兴趣的油膜区域提取效果对比

    Tab.  3  Comparison of region of interest extraction results of different methods

    实验条件处理方法F-Measure每帧处理时间/s
    平静海面GMM0.0640.458
    WEBS0.9330.328
    SFS0.9520.943
    本文0.9730.194
    波浪干扰GMM0.0680.461
    WEBS0.9360.366
    SFS0.9570.924
    本文0.9690.195
    漂浮物干扰GMM0.0670.477
    WEBS0.8970.343
    SFS0.8630.947
    本文0.9110.208
    下载: 导出CSV
  • [1] Lehr W J, Cekirge H M, Fraga R J, et al. Empirical studies of the spreading of oil spills[J]. Oil and Petrochemical Pollution, 1984, 2(1): 7−11. doi: 10.1016/S0143-7127(84)90637-9
    [2] Leifer I, Lehr W J, Simecek-Beatty D, et al. State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater Horizon oil spill[J]. Remote Sensing of Environment, 2012, 124: 185−209. doi: 10.1016/j.rse.2012.03.024
    [3] Svejkovsky J, Hess M, Muskat J, et al. Characterization of surface oil thickness distribution patterns observed during the Deepwater Horizon (MC-252) oil spill with aerial and satellite remote sensing[J]. Marine Pollution Bulletin, 2016, 110(1): 162−176. doi: 10.1016/j.marpolbul.2016.06.066
    [4] 李四海. 海上溢油遥感探测技术及其应用进展[J]. 遥感信息, 2004(2): 53−57. doi: 10.3969/j.issn.1000-3177.2004.02.015

    Li Sihai. Application of remote sensing for oil slicks detecting and its progress[J]. Remote Sensing Information, 2004(2): 53−57. doi: 10.3969/j.issn.1000-3177.2004.02.015
    [5] Fingas M, Brown C E. A review of oil spill remote sensing[J]. Sensors, 2017, 18(2): 91.
    [6] 景海朋. 基于红外图像的水面溢油检测及系统实现[D]. 西安: 西安电子科技大学, 2014.

    Jing Haipeng. Surface oil spill detection and system implementation based on infrared image[D]. Xi’an: Xidian University, 2014.
    [7] 蒋兴伟, 何贤强, 林明森, 等. 中国海洋卫星遥感应用进展[J]. 海洋学报, 2019, 41(10): 113−124.

    Jiang Xingwei, He Xianqiang, Lin Mingsen, et al. Progresses on ocean satellite remote sensing application in China[J]. Haiyang Xuebao, 2019, 41(10): 113−124.
    [8] 任广波, 过杰, 马毅, 等. 海面溢油无人机高光谱遥感检测与厚度估算方法[J]. 海洋学报, 2019, 41(5): 146−158.

    Ren Guangbo, Guo Jie, Ma Yi, et al. Oil spill detection and slick thickness measurement via UAV hyperspectral imaging[J]. Haiyang Xuebao, 2019, 41(5): 146−158.
    [9] Lacava T, Ciancia E, Coviello I, et al. A MODIS-based robust satellite technique (RST) for timely detection of oil spilled areas[J]. Remote Sensing, 2017, 9(2): 128. doi: 10.3390/rs9020128
    [10] Shu Yuanming, Li J, Yousif H, et al. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9): 2026−2035. doi: 10.1016/j.rse.2010.04.009
    [11] 陈韩, 谢涛, 方贺, 等. 基于SAR极化比和纹理特征的海面溢油识别方法[J]. 海洋学报, 2019, 41(9): 181−190.

    Chen Han, Xie Tao, Fang He, et al. Sea surface oil spill identification method based on SAR polarization ratio and texture feature[J]. Haiyang Xuebao, 2019, 41(9): 181−190.
    [12] 尹奇志, 初秀民, 孙星, 等. 船舶溢油监测方法的应用现状及发展趋势[J]. 船海工程, 2010, 39(5): 246−250.

    Yin Qizhi, Chu Xiumin, Sun Xing, et al. Application and development trend of detecting techniques for oil slick from vessel[J] Ship & Ocean Engineering, 2010, 39(5): 246−250.
    [13] 张果. 近距离海面溢油监测系统的设计与实现[D]. 西安: 西安电子科技大学, 2018.

    Zhang Guo. Design and implementation of close-range sea surface spill oil monitoring system[D]. Xi’an: Xidian University, 2018.
    [14] Bagavathiappan S, Lahiri B B, Saravanan T, et al. Infrared thermography for condition monitoring—A review[J]. Infrared Physics & Technology, 2013, 60: 35−55.
    [15] De Carolis G, Adamo M, Pasquariello G. On the estimation of thickness of marine oil slicks from sun-glittered, near-infrared MERIS and MODIS imagery: the Lebanon oil spill case study[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 559−573. doi: 10.1109/TGRS.2013.2242476
    [16] Niclòs R, Doña C, Valor E, et al. Thermal-infrared spectral and angular characterization of crude oil and seawater emissivities for oil slick identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5387−5395. doi: 10.1109/TGRS.2013.2288517
    [17] Lu Yingcheng, Zhan Wenfeng, Hu Chuanmin. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment[J]. Remote Sensing of Environment, 2016, 181: 207−217. doi: 10.1016/j.rse.2016.04.007
    [18] Zhou Yang, Jiang Lu, Lu Yingcheng, et al. Thermal infrared contrast between different types of oil slicks on top of water bodies[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1042−1045. doi: 10.1109/LGRS.2017.2694609
    [19] Akula A, Ghosh R, Kumar S, et al. Moving target detection in thermal infrared imagery using spatiotemporal information[J]. Journal of the Optical Society of America A, 2013, 30(8): 1492−1501. doi: 10.1364/JOSAA.30.001492
    [20] 李艳荻, 徐熙平. 基于超像素时空特征的视频显著性检测方法[J]. 光学学报, 2019, 39(1): 315−322.

    Li Yandi, Xu Xiping. Video saliency detection method based on spatiotemporal features of superpixels[J]. Acta Optica Sinica, 2019, 39(1): 315−322.
    [21] 王利锋, 辛丽平, 于波, 等. 基于热红外图像的海面油膜面积的测算方法[J]. 海洋通报, 2020, 39(6): 750−760.

    Wang Lifeng, Xin Liping, Yu Bo, et al. A calculation method for oil film area at sea surface based on thermal infrared image[J]. Marine Science Bulletin, 2020, 39(6): 750−760.
    [22] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62−66. doi: 10.1109/TSMC.1979.4310076
    [23] 刘伯运, 赵博, 王腾. 基于连续帧图像面积变化的火灾探测方法[J]. 消防科学与技术, 2016, 35(12): 1723−1725. doi: 10.3969/j.issn.1009-0029.2016.12.022

    Liu Boyun, Zhao Bo, Wang Teng. A fire detection method based on the area variety of consecutive frames[J]. Fire Science and Technology, 2016, 35(12): 1723−1725. doi: 10.3969/j.issn.1009-0029.2016.12.022
    [24] 刘松涛, 刘振兴, 姜宁. 基于融合显著图和高效子窗口搜索的红外目标分割[J]. 自动化学报, 2018, 44(12): 2210−2221.

    Liu Songtao, Liu Zhenxing, Jiang Ning. Target segmentation of infrared image using fused saliency map and efficient subwindow search[J]. Acta Automatica Sinica, 2018, 44(12): 2210−2221.
  • 加载中
图(15) / 表(3)
计量
  • 文章访问数:  70
  • HTML全文浏览量:  16
  • PDF下载量:  17
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-17
  • 修回日期:  2021-11-13
  • 网络出版日期:  2022-01-17
  • 刊出日期:  2022-06-15

目录

    /

    返回文章
    返回