红外视频中的舰船检测

石超, 陈恩庆, 齐林. 红外视频中的舰船检测[J]. 光电工程, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748
引用本文: 石超, 陈恩庆, 齐林. 红外视频中的舰船检测[J]. 光电工程, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748
Shi Chao, Chen Enqing, Qi Lin. Ship detection from infrared video[J]. Opto-Electronic Engineering, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748
Citation: Shi Chao, Chen Enqing, Qi Lin. Ship detection from infrared video[J]. Opto-Electronic Engineering, 2018, 45(6): 170748. doi: 10.12086/oee.2018.170748

红外视频中的舰船检测

  • 基金项目:
    国家自然科学基金资助项目(61331021);河南省重点科技攻关项目(152102310294);河南省产学研项目(162107000023)
详细信息
    作者简介:
    通讯作者: 陈恩庆(1977-),男,博士,教授,主要从事模式识别、图像处理的研究。E-mail: ieeqchen@zzu.edu.cn
  • 中图分类号: TP391

Ship detection from infrared video

  • Fund Project: Supported by National Natural Science Foundation (61331021), the Key Science and Technology Program of Henan Province(152102310294), and the Industry-University-Research Collaboration Program of Henan Province(162107000023)
More Information
  • 红外视频中的舰船目标检测在渔政管理、港口监控等领域具有广泛的应用价值。传统的背景减除方法,如高斯混合模型(GMM)、码本算法(Codebook)和ViBe算法等,在海面红外视频舰船检测过程中容易受到海浪的影响导致错误检测。本文提出一种新的算法框架实现红外海面视频中的舰船检测任务。该算法框架采用了Top-Hat操作对红外图像进行预处理,从而有效过滤杂波,随后应用改进ViBe算法完成对舰船目标的检测。实验结果表明,本文算法可以有效抑制背景噪声,取得了较好的检测效果。

  • Overview: Moving target detection on the sea surface is a very complicated work. Due to the complex environment of the surface, there are a lot of clutter, so it is difficult to detect the ship on the sea surface. Most of the moving object detection algorithms are applicable to the field of visible light, but visible light imaging is only suitable for daytime work and cannot continuously detect the target at night. Infrared camera can continue to work at night, so it has certain advantages compared with the visible light camera. At present, the moving target detection based on infrared video is a popular research direction. The ship detection from infrared video has wide application value in fishery administration, port monitoring and other fields. In the infrared sea video, the background is more complicated and the waves are irregular, which undoubtedly will increase the difficulty of detecting the ship. Traditional background modeling methods contain Gaussian Mixture Model, Codebook and ViBe, etc. In the process of ship detection from the infrared video, the Gaussian Mixture Model is easy to produce hollow, and it will lead to the detected ship to be incomplete; The Codebook only can adapt to the background of the small periodic motion, unable to cope with the irregular change of the sea clutter; ViBe which uses the single frame for background modeling, can quickly detect foreground targets, but it is prone to ghost and cannot deal with the complex sea environment. Above these algorithms are easily affected by the waves leading to false detection. So this paper proposes a new algorithm framework to detect ship from the infrared video. First of all, the algorithm adopts a Top-Hat operation for preprocessing of the infrared images to suppress the background clutter effectively and highlight the ship target, and then improves ViBe to detect the moving ship target. The paper using adaptive threshold replaces the original fixed threshold of Vibe to deal with the complex environment of the sea surface. In addition, the paper introduces the color distortion as the judgment standard to complete the ship target detection. In the experiment of FPS contrast chart, the algorithm can achieve real-time requirements. In addition, the experimental results show that the algorithm can effectively suppress background noise, and the selection of adaptive threshold can better cope with the complex and varied waves on the sea. Compared with other algorithms, it is shown that the proposed algorithm can achieve better detection results and robustness.

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  • 图 1  Top-Hat滤波

    Figure 1.  Top-Hat filter

    图 2  二维欧氏空间像素分类(C1C2)

    Figure 2.  2D European space pixel classification (C1, C2)

    图 3  Codebook颜色模型

    Figure 3.  Codebook color model

    图 4  算法流程图

    Figure 4.  Flow chart of the algorithm

    图 5  单一舰船场景下前景背景分割结果。(a)输入视频帧;(b)高斯混合模型;(c) ViBe算法;(d) Codebook算法;(e) KDE算法;(f)本文算法

    Figure 5.  Foreground and background segmentation results of a single ship. (a) The input image; (b) GMM; (c) ViBe; (d) Codebook; (e) KDE; (f) Our method

    图 6  岛屿及单一舰船场景下前景背景分割结果。(a)输入视频帧;(b)高斯混合模型;(c) ViBe算法;(d) Codebook算法;(e) KDE算法;(f)本文算法

    Figure 6.  Foreground and background segmentation results of a single ship and island. (a) The input image; (b) GMM; (c) ViBe; (d) Codebook; (e) KDE; (f) Our method

    图 7  岛屿及多舰船环境下前景背景分割结果。(a)输入视频帧;(b)高斯混合模型;(c) ViBe算法;(d) Codebook算法;(e) KDE算法;(f)本文算法

    Figure 7.  Foreground and background segmentation results of island and many ships. (a) The input image; (b) GMM; (c) ViBe; (d) Codebook; (e) KDE; (f) Our method

    图 8  5种背景建模法的FPS的对比图

    Figure 8.  FPS contrast chart of five background modeling methods

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出版历程
收稿日期:  2018-01-01
修回日期:  2018-02-21
刊出日期:  2018-06-01

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