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Real-time geometry identification of moving ships by computer vision techniques in bridge area

机译:利用计算机视觉技术实时识别桥梁区域中动船的几何形状

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摘要

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.
机译:作为结构健康监测系统的一部分,船与桥之间的相对几何关系对于桥梁主管部门和船东避免船桥碰撞至关重要。这项研究提出了一种新颖的计算机视觉方法,该方法通过使用转移学习技术和单眼视觉技术,基于单发多盒检测器(SSD)实时识别移动船舶的几何参数。识别框架包括船舶检测(粗尺)和几何参数计算(细尺)模块。对于船舶检测,采用了一种深度学习算法SSD,并通过从Internet下载的船舶图像样本对其进行了微调,以粗略地获得感兴趣的矩形区域。随后,为了进行几何参数计算,在色相,饱和度和值颜色空间中使用饱和度通道内的形态学操作创建了精确的船形轮廓。此外,使用射影几何变换构造了局部坐标系,以计算船舶的几何参数,例如宽度,长度,高度,位置和速度。将该方法应用于从航道上方武汉长江大桥大梁上设置的摄像机获得的现场视频图像,证实了该方法的有效性,准确性和有效性。

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