Abstract
Using X-ray weld defect images for defects detection is a very significant method for non-destructive testing (NDT). Traditionally, this work should be done by skilled technicians who are time-consumed and easily influenced by the environment. Many efforts have been made on automatic classification. However their work either need manual features specified by technicians or get a low accuracy. Some datasets they used for testing are too small to validate the generative capacity. In this paper, we propose a VGG16 based fully convolutional structure to classify the weld defect image, which achieves a high accuracy with a relative small dataset for deep learning method. We choose a dataset with 3000 images for testing the generative capacity of our network, which is large enough compared to others methods. Using this method, we got a \(97.6\%\) test accuracy and \(100\%\) train accuracy through our network on two main defects. The time used for each patch is about 0.012 s, which is faster than others methods.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (61527804, 61301116, 61521062, 6113300961771306), Chinese National Key S&T Special Program (2013ZX01033001-002-002), the 111 Project (B07022), the Shanghai Key Laboratory of Digital Media Processing and Transmissions (STCSM 12DZ2272600).
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Liu, B., Zhang, X., Gao, Z., Chen, L. (2018). Weld Defect Images Classification with VGG16-Based Neural Network. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_20
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DOI: https://doi.org/10.1007/978-981-10-8108-8_20
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