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基于L0梯度平滑与图像分块聚类的海天线检测
郑兵,董超,胡海驹,陈焱琨,刘蔚
0
(国家海洋局南海调查技术中心,广东 广州 510300;自然资源部海洋环境探测技术与应用重点实验室,广东 广州 510300;南方海洋科学与工程广东省实验室,广东 珠海 440402;广东省国土资源测绘院,广东 广州 510500)
摘要:
海天线检测在海洋工程安防活动中具有重要的意义,真实海洋环境中的海天线检测易受云朵、海浪、光照变化、目标遮挡物、边界模糊等外界干扰。为了实现对真实海洋环境中海天线的检测,本研究提出一种基于L0梯度平滑和图像分块聚类的海天线检测算法。首先,对图像进行L0梯度平滑滤波,以增强海天线边缘,弱化非海天线因素干扰;接着,将图像沿着竖直方向分割成若干等宽图像块,以降低整体环境干扰,加强局部海天线检测效果;然后,通过Canny算子和霍夫变换提取每个分割图像块中的直线段;最后,采取K-means聚类算法提取每个图像块中的海天线段,拟合生成完整海天线。实验结果表明,在真实的海天线数据集中,本研究方法获取的矩形框重叠率平均精度为93.22%,角度差平均精度为7.66%,均高于文中选取的近年典型对比算法。满足实际海天线检测抗干扰强、准确率高、适应性广等要求。
关键词:  海洋水文学  海天线检测  L0梯度平滑滤波  图像分块  K-means线段聚类
DOI:10.3969/J.ISSN.2095-4972.20220726001
基金项目:自然资源部海洋环境探测技术与应用重点实验室自主设立课题(MESTA-2021-C004);海洋科学技术局长基金(180214);南方海洋科学与工程广东省实验室(珠海)项目(SML2021SP205)
Sea-sky-line detection base on L0 gradient smoothing and image segmentation clusters
ZHENG Bing,DONG Chao,HU Haiju,CHEN Yankun,LIU Wei
(South China Sea Marine Survey and Technology Center, SOA, Guangzhou 510300, China;Key Laboratory of Marine Environmental Survey Technology and Application, MNR, Guangzhou 510300, China;Southern Marine Science Engineering Guangdong Laboratory, Zhuhai 440402, China;Institute of Surveying and Mapping, Department of Natural Resources of Guangdong Province, Guangzhou 510500, China)
Abstract:
Sea-sky-line detection is of great significance in the security of marine engineering activities. Sea-sky-line detection is susceptible to external interference in real marine environment such as clouds, waves, illumination variation, target occlusions and boundary blur, etc. A sea-sky-line detection algorithm based on L0 gradient smoothing and image segmentation & clusters is proposed. Firstly, the image is filtered by L0 gradient smoothing to enhance sea-sky-lines edge and weaken the interference of non-sea-sky-lines. Then, the image is segmented into several equal-width image blocks along vertical direction to reduce environmental interference and strengthen the detection effect of local sea-sky-lines. The straight line segments in each segmented image block are extracted by Canny operator and Hough transform. Finally, K-means clustering algorithm is adopted to extract the sea-sky-line in each image block, thus fitting to generate the final sea-sky-line. Experimental results show that average accuracy of bounding box overlap rate is 93.22% and the average accuracy of angle difference ration is 7.66% in the real sea-sky-line dataset, both of which are higher in comparison with typical algorithms selected in recent years. Result meets the requirements of real sea-sky-line detection with characters of strong anti-interference, high accuracy and wide adaptability.
Key words:  marine hydrology  sea-sky-line detection  L0 gradient smoothing  image segmentation  K-means linear cluster

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