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摘要
海面波浪、船只与光照等因素的影响，使得可见光海面图像中的海天线难以被准确检测。为提高海天线检测的准确性与鲁棒性，提出了基于局部Otsu分割与Hough变换的海天线检测方法。首先，通过纵向中值滤波快速地抑制灰度图像中的光斑等高频噪声。然后，根据图像特点进行纵向分块处理来补偿光照的不均匀性并将船只的干扰范围限定在部分图像块中，再进行局部Otsu分割得到二值图像并提取其中的边缘像素，抑制了波浪边缘的干扰。最后，采用Hough变换拟合边缘像素以得到海天线。实验结果表明所提方法具有较高的准确性、鲁棒性与实时性，其检测准确率达93.0%，显著高于三种代表性的海天线检测方法。
Abstract
Due to the interference such as sea waves, ships and light, it is difficult to accurately detect the sea-sky-line of the visible light maritime image. To improve the detection accuracy and robustness, a sea-sky-line detection method based on local Otsu segmentation and Hough transform is proposed. Firstly, high-frequency noise such as light spot in the gray image is rapidly suppressed by longitudinal median filter. Then, according to the image features, the gray image is divided into image blocks in longitudinal to compensate for inhomogeneity of illumination and limit the interference scope of ships to some image blocks. Afterwards, local Otsu segmentation is performed on the gray image to obtain the binary image where edge pixels are extracted, which suppresses the interference of waves. Finally, Hough transform is used to fit edge pixels to complete the sea-sky-line detection. Experimental results show that the proposed method is relatively accurate, robust and real-time. The detection accuracy of the proposed method is 93.0%, which is significantly higher than that of three representative sea-sky-line detection methods.
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Overview
Overview: Unmanned surface vehicle (USV) has a great potential to play an important role in the near future, such as sea environmental monitoring and maritime rescue. USV obtains information about surrounding sea surface environment by processing the visible light maritime image from the camera mounted on the USV. Sea-sky-line detection is useful in the visible light maritime image processing. It can provide important reference for the target detection and image calibration. Existing sea-sky-line detection methods are mainly used in infrared maritime images with simple scenes and less interference. In contrast, there are few studies on sea-sky-line detection in complex visible light maritime images. There are two main methods for the detection of sea-sky-line, namely the method based on line extraction from edge pixels and the method based on image segmentation. However, the former method is susceptible to the gradient change of sea waves and sea-sky-line, while the latter is limited by the accuracy of image segmentation. Due to the interference such as sea waves, ships and light, it is difficult to accurately detect the sea-sky-line of the visible light maritime image. To improve the detection accuracy and robustness, a sea-sky-line detection method based on local Otsu segmentation and Hough transform is proposed. Firstly, high-frequency noise such as light spot in the gray image is rapidly suppressed by longitudinal median filter. Then, according to the image features, local Otsu segmentation is performed to obtain binary images where edge pixels are extracted. Finally, Hough transform is used to fit edge pixels to complete the sea-sky-line detection. In the proposed method, image block processing compensates for the inhomogeneity of illumination and limits the interference scope of ships to some image blocks, which makes the local Otsu segmentation more accurate than the global Otsu segmentation. In addition, compared with the edge detection of the sea-sky-line based on the gradient, the edge detection of the sea-sky-line based on image segmentation can better adapt to the change of the image gradient and suppress the interference of the wave edge. Hough transform can ensure the accurate fitting of the sea-sky-line from the edge pixel if the number of edge pixels extracted of the sea-sky-line is more than half of the image width. Experimental results show that the interference of sea waves, ships and light can be effectively suppressed by the proposed method, which is relatively accurate, robust and real-time. The sea-sky-line detection accuracy of the proposed method is 93.0%, which is significantly higher than that of three representative sea-sky-line detection methods.
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图 1 基于局部Otsu分割与Hough变换的海天线检测流程
Figure 1. Flowchart of sea-sky-line detection based on local Otsu segmentation and Hough transform
图 3 典型海面图像的分割结果对比图。(a)~(d)原始图像；(e)~(h)全局Otsu阈值分割结果；(i)~(l)局部Otsu阈值分割结果
Figure 3. Segmentation results comparison of typical maritime images. (a)~(d) Original images; (e)~(h) Results of global Otsu segmentation; (i)~(l) Results of local Otsu segmentation
图 4 典型海面图像的边缘提取结果对比图。(a)~(d)低梯度阈值Canny边缘提取结果；(e)~(h)高梯度阈值Canny边缘提取结果；(i)~(l)基于局部Otsu分割的边缘提取结果
Figure 4. Edge detection results comparison of typical maritime images. (a)~(d) Edge detection results of Canny with low gradient threshold; (e)~(h) Edge detection results of Canny with high gradient threshold; (i)~(l) Edge detection results based on local Otsu segmentation
图 5 典型海面图像中本文方法的海天线检测结果。(a)~(d)海浪干扰图像；(e)~(h)模糊图像；(i)~(l)反光干扰图像；(m)~(p)近景船只干扰图像
Figure 5. Sea-sky-line detection results of the proposed method on typical maritime images. (a)~(d) Images with many waves; (e)~(h) Blurred images; (i)~(l) Images with sea reflection; (m)~(p) Images with close target
图 6 海天线检测方法的测试结果对比图。(a)~(d) Prasad方法的测试结果；(e)~(h) Fefilatyev方法的测试结果；(i)~(l) Kristan方法的测试结果
Figure 6. Sea-sky-line detection results comparison of three methods. (a)~(d) Results of Prasad method; (e)~(h) Results of Fefilatyev method; (i)~(l) Results of Kristan method
图 7 失败案例。(a)海天线被严重遮蔽的图像；(b) 图 7(a)的局部Otsu分割结果；(c)海天区域模糊不清的图像；(d) 图 7(c)的局部Otsu分割结果
Figure 7. Failure cases. (a) The image that sea-sky line is shaded; (b) The local Otsu segmentation results of image 7(a); (c) The image that sea-sky region is blurred; (d) The local Otsu segmentation results of image 7(c)
表 1 100帧测试图像的海天线检测结果对比
Table 1. Sea-sky-line detection result comparison of 100 frames test images
Prasad method Fefilatyev method Kristan method Proposed method Detection rate/% 77.0 72.0 64.0 93.0 Time consumed per frame/ms 685 370 267 216 
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