The draft is a measurement of the vertical distance between the waterline and the bottom of the ship hull. The displacement tonnage of a ship then can be calculated by the observed draft. The current draft survey is done by surveyors, which is subject to human errors. We propose to use computer vision with deep learning for draft reading from images. First, mask R-CNN is used to segment the region of interest—draft marks and water—from images. Then UNet is used to refine the waterline detection. The detection of marks is based on the computer vision methods and the content of marks is recognized by ResNet. Finally, we can infer the draft of a ship based on the extracted visual information. Experimental results on a realistic dataset have shown that the proposed method can perform the task of draft reading on a par with humans.

中文翻译：

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具有深度学习的计算机视觉，可用于阅读船舶吃水

吃水深度是水线与船体底部之间的垂直距离的度量。然后，可以通过观察到的吃水深度计算出船舶的排水吨位。当前的调查草稿是由验船师完成的，存在人为错误。我们建议将计算机视觉与深度学习结合使用，以从图像中读取草稿。首先，使用遮罩R-CNN从图像中分割出感兴趣的区域（草稿标记和水）。然后使用UNet完善水线检测。标记的检测基于计算机视觉方法，并且ResNet可以识别标记的内容。最后，我们可以根据提取的视觉信息推断出船舶的吃水深度。在真实数据集上的实验结果表明，所提出的方法可以完成与人类同等程度的草稿阅读任务。