A Weld Seam Dataset and Automatic Detection of Welding Defects Using Convolutional Neural Network

  • Wenming Guo
  • Lin HuangEmail author
  • Lihong Liang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


In this paper, we propose a dataset which contains 13006 digitalized x-ray images of welds. And do some preparation work on the original images which can be used to put into the convolutional neural network. Firstly, because of the feature of the input is the original image, but defects of welds are very small in the whole diagram, so that cut up the welds area. Secondly, do some picture preprocessing which includes data enhancement, image regularization, mean subtraction, normalization, etc. Then a model which is built to train and then test the weld images cropped from x-ray images is constructed based on convolutional neutral network. Different from the results ever achieved, this model directly using the feature between pixels and pixels of images without extra extraction of the image feature. Finally, tell the procedure when it comes to train dataset and test dataset, compare the different result of the different image preprocessing, we propose several experiments and results. The results demonstrate that what kind of preprocessing method is better and to do the classification of the picture.


Weld image Dataset Deep learning Weld seam detection Convolutional neural network 



This research was supported by the National key foundation for exploring scientific instrument of China (Project No. 2013YQ240803).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.China Special Equipment Inspection and Research InstituteBeijingChina

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