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Circular CNN Based Auto-encoding Deep Network for Segmentation of a Welded Circular Seam of Pipeline from an Image Captured by a Camera

机译:基于循环CNN的自动编码深网络,用于从摄像机捕获的图像中的管道焊接圆接缝的分割

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It is an important part of an intelligent pipeline nondestructive testing robot to detect the welded circular seams from a frame image which is captured by the robot’s camera. By use of prior knowledge of the contour shape of a welded circular seam, a circular convolution along a circular ring is deigned and an auto-encoding deep learning network is proposed based the circular convolutional neural network this paper. In the stage of training the auto-encoding network, a training frame image is automatically partitioned into concentricity rings. Each ring is visually and easily notated as a welded seam ring or a background ring and then used as a sample to train the network. In the stage of testing, the testing frame is automatically partitioned into concentricity rings according to the sample frame and then the each ring is auto-encoded and recognized if it is welded seam ring. The welded seam is thus coarsely segmented by the circular convolution based auto-encoding deep network. Then OTSU is used to segment out the welded seam from the welded seam ring finely and effectively. The experimental results show that the proposed circular convolution based auto-encoding deep network and segmentation algorithm are effective and efficient.
机译:它是智能管道非破坏性测试机器人的重要组成部分,用于从机器人的相机捕获的帧图像中检测焊接圆形接缝。通过使用先验知识的焊接圆周的轮廓形状,沿圆环的圆形卷积是基于本文的圆形卷积神经网络的自动编码的深度学习网络。在训练自动编码网络的阶段,训练帧图像自动分隔成同心的环。每个环在视觉上且容易被注意为焊接缝环或背景环,然后用作培训网络的样本。在测试阶段,根据样品框架,测试框架自动分隔成同心的环,然后在焊接缝环焊接接缝环时自动编码并识别每个环。因此,焊接接缝由基于圆形卷积的自动编码的深网络粗略地分割。然后,OTSU用于精细且有效地将焊接接缝从焊缝环分段。实验结果表明,所提出的基于循环卷积的自动编码深网络和分割算法是有效且有效的。

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