[1]程健,安鸿波,郭一楠,等.一种区域知识引导的船舶吃水线动态识别算法[J].郑州大学学报(工学版),2021,42(03):47.[doi:10.13705/j.issn.1671-6833.2021.03.008]
　Cheng Jian,An Hongbo,Guo Yinan,et al.Dynamic Identification of Ship Waterline Image Area Based on Knowledge Guidance[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):47.[doi:10.13705/j.issn.1671-6833.2021.03.008]

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一种区域知识引导的船舶吃水线动态识别算法()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42

期数:

2021年03期

页码:

47

栏目:

出版日期:

2021-05-10

文章信息/Info

Title:

Dynamic Identification of Ship Waterline Image Area Based on Knowledge Guidance

作者:

程健; 安鸿波; 郭一楠; 叶亮

煤炭科学研究总院矿山大数据研究院;中国矿业大学信息与控制工程学院;中国矿业大学(北京)机电与信息工程学院;

Author(s):

Cheng Jian; An Hongbo; Guo Yinan; Ye Liang;

The Institute of Mining of the General Institute of Coal Sciences; School of Information and Control Engineering, China University of Mining and Technology; School of Electromechanical and Electromechanical Engineering, China University of Mining and Technology (Beijing);

关键词:

航拍图像; 吃水线区域; 轮廓统计筛选; 知识引导; 动态识别

Keywords:

aerial image;  waterline area;  statistic-based selection for contour;  knowledge guidance;  dynamic identification

DOI:

10.13705/j.issn.1671-6833.2021.03.008

文献标志码:

A

摘要:

用于船舶吃水线识别的机器视觉检测方法采用固定相机获取水尺区域,存在船舶外弦吃水线图像数据采集难度大¸吃水线测定效率低等问题基于此,利用无人机搭载相机航拍图像,充分考虑其图像特点,提出一种吃水线区域动态识别算法无人机航拍图像克服了已有固定相机的不足,但是,由于无人机飞行航迹的变化导致吃水线图像存在波动,此外,吃水线区域的水迹线¸水波纹以及不同曝光环境也会影响吃水线的识别精度,为此,首先采用融合水尺特性先验知识的轮廓统计筛选方法,提取先导知识,定位整幅图像中感兴趣的吃水线水平区域进而综合利用像素点在Lxaxb颜色空间的信息,采用知识引导的k-meanz++聚类和分水岭算法,实现吃水线的精确分割面向多场景下的航拍图像,实验结果表明,所提算法可以有效避免光照条件和水迹线等的干扰,实现动态视频下船舶吃水线区域的快速识别,对港口这一复杂航拍场景具有较强的环境鲁棒性,为准确实时水尺计重提供有效保障

Abstract:

In traditional image-based waterline identification method, a fixed camera is used to capture the waterline area, which requires complex equipments and has difficulty to measure the waterline of the outer chord of ships. This will decrease the identification efficiency. Based on this, a dynamic identification method of waterline area based on aerial image captured by UAV is proposed. Though UAV is more convenient than the fixed cameras, the waterline area in the images may be unsettled due to the unstable flight track. In addition, water trace, ripples in the surface of water and different exposure environment all have the adverse impact on the recognition of waterline. Thus, a statistic-based selection for contour combined with the prior knowledge of waterline characteristics is adopted to locate the interesting horizontal areas containing the waterline. After comprehensively utilizing the color and spatial information of pixels, the waterline is accurately segmented by knowledge-guided K-means++ and watershed algorithm under L×a×b color space. The experimental results show that the proposed method can effectively process the influence from light conditions, water traces and so on, as well as rapidly identify the waterline area from a dynamic video. Its strong robustness for the complex environment in ports provides the foundation for obtaining the accurate weigh of cargoes.

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