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Robust detection of sea mines in side-scan sonar imagery based on advanced gray-scale morphological filters

机译:基于高级灰度形态学滤波器的侧扫声纳图像中的矿山鲁棒检测

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Abstract: Computing Devices Canada, a General Dynamics company, undertakes research in image processing focusing on the automatic recognition of sea mines. This paper presents the use of advanced gray-scale morphological filters for the detection of sea mines in side-scan sonar imagery. Sea mines in side-scan sonar imagery can be characterized by a mine-body and a mine shadow. Mine-bodies consist of bright regions, relative to the background, with a specific shape and size. Mine-shadows consist of dark regions, relative to the background, with a specific shape and size. The shapes and sizes of these regions depend on the mine type, the orientation of the mine, the physical acquisition process of the sonar imagery, and the environment in which the mine is located. Advanced gray-scale morphological filters provide very powerful and robust tools to extract bright and dark regions with low signal to noise ratio in very noisy imagery using geometric constraints such as shape, size and total surface area. For the detection of sea mines we use these morphological filters with the minimum and maximum geometric constraints for the mine-bodies and mine-shadows. The independent detection of mine-bodies and mine-shadows allows the detection of bottom, moored and drifting mines with the same detection algorithm. Consistent mine-body and mine-shadow combinations are resolved into mine like objects. !9
机译:摘要:加拿大通用设备公司(Computer Devices Canada)是一家通用动力公司,致力于图像处理的研究,重点是自动识别雷区。本文介绍了使用先进的灰度形态学过滤器在侧面扫描声纳图像中检测海雷的方法。侧面扫描声纳图像中的地雷的特征是地雷体和地雷阴影。地雷由相对于背景的明亮区域组成,具有特定的形状和大小。矿井阴影由相对于背景的暗区域组成,具有特定的形状和大小。这些区域的形状和大小取决于地雷的类型,地雷的方向,声纳图像的物理采集过程以及地雷所在的环境。先进的灰度形态过滤器提供了非常强大而强大的工具,可以使用诸如形状,大小和总表面积之类的几何约束,在噪声很大的图像中以低信噪比提取亮区和暗区。为了检测地雷,我们使用这些形态过滤器对矿体和矿井阴影具有最小和最大的几何约束。地雷和阴影的独立检测允许使用相同的检测算法来检测地雷,系泊和漂移的地雷。一致的防雷体和防雷阴影组合被解析为类似于防雷的对象。 !9

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