Using Deep Learning for Defect Classification on a Small Weld X-ray Image Dataset

Author:

Ajmi Chiraz,Zapata JuanORCID,Martínez-Álvarez José Javier,Doménech Ginés,Ruiz Ramón

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Reference35 articles.

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3. Carvalho, A., Rebello, J., Sagrilo, L., Camerini, C., Miranda, I.: Mfl signals and artificial neural networks applied to detection and classification of pipe weld defects. Ndt & E Int. 39(8), 661–667 (2006)

4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 886–893 (2005). http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1467360&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1467360

5. Di, L., Yonglun, S., Feng, Y.: Online monitoring of weld defects for short-circuit gas metal arc welding based on the self-organizing feature map neural networks. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 5, pp. 239–244 (2000)

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