Filtragem de Dados de Treinamento para Aprendizagem Profunda Aplicada à Inspeção de Juntas Soldadas em Tubulações de Petróleo
Resumo
A inspeção de radiografias de soldas é uma tarefa essencial para a prevenção de vazamentos em tubulações da indústria petrolífera. A inspeção automática é importante para auxiliar os especialistas em uma tarefa que pode ser influenciada pela fadiga, acuidade visual e experiência. Uma dificuldade da inspeção automática é a disponibilidade de imagens laudadas que possam ser utilizadas para o treinamento de classificadores. O uso de aumento de dados auxilia nesta tarefa mas pode produzir imagens com pouco impacto ou mesmo impacto negativo no treinamento de um classificador. Neste artigo apresenta-se uma método para filtragem de imagens, de forma a favorecer o reaprendizado do classificador, processo este que se dá pela eliminação de imagens com baixo desempenho na base de treino e posterior retreinamento, com impacto positivo para a generalização do modelo de classificação. Os resultados experimentais do método mostram que a filtragem dos dados acelera o processo de aprendizado e produz uma melhora no desempenho da classificação.
Palavras-chave:
Redes Neurais Artificiais, Aprendizagem Profunda, Visão Computacional
Referências
Atlas, R. M. e Hazen, T. C. (2011). Oil biodegradation and bioremediation: a tale of the two worst spills in us history.
Baniukiewicz, P. (2014). Automated defect recognition and identification in digital radiography. Journal of Nondestructive Evaluation, 33(3):327–334.
Davis, J. R. (1989). ASM Handbook: Nondestructive evaluation and quality control, volume 17. ASM International.
Duan, F., Yin, S., Song, P., Zhang, W., Zhu, C., e Yokoi, H. (2019). Automatic welding defect detection of x-ray images by using cascade adaboost with penalty term. IEEE Access, 7:125929–125938.
Fucsok, F., Muller, C., e Scharmach, M. (2002). Reliability of routine radiographic film evaluation–an extended roc study of the human factor. In 8th European Conference on Non Destructive Testing, Barcelona, June, pages 17–21.
He, K., Zhang, X., Ren, S., e Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Krizhevsky, A., Sutskever, I., e Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
Liao, T. W. (2003). Classification of welding flaw types with fuzzy expert systems. Expert Systems with Applications, 25(1):101–111.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., e Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988.
Liu, B., Zhang, X., Gao, Z., e Chen, L. (2017). Weld defect images classification with vgg16-based neural network. In International Forum on Digital TV and Wireless Multimedia Communications, pages 215–223. Springer.
Mery, D. e Arteta, C. (2017). Automatic defect recognition in x-ray testing using computer vision. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 1026–1035. IEEE.
Nacereddine, N. e Tridi, M. (2005). Computer-aided shape analysis and classification of weld defects in industrial radiography based invariant attributes and neural networks. In ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pages 88–93. IEEE.
Rana, S. et al. (2008). Facts and data on environmental risks-oil and gas drilling operations. In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers.
Ronneberger, O., Fischer, P., e Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Shorten, C. e Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60.
Simonyan, K. e Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., e Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Wang, Y., Shi, F., e Tong, X. (2019). A welding defect identification approach in x-ray images based on deep convolutional neural networks. In International Conference on Intelligent Computing, pages 53–64. Springer.
Zapata, J., Vilar, R., e Ruiz, R. (2012). Automatic inspection system of welding radiographic images based on ann under a regularisation process. Journal of Nondestructive Evaluation, 31(1):34–45.
Baniukiewicz, P. (2014). Automated defect recognition and identification in digital radiography. Journal of Nondestructive Evaluation, 33(3):327–334.
Davis, J. R. (1989). ASM Handbook: Nondestructive evaluation and quality control, volume 17. ASM International.
Duan, F., Yin, S., Song, P., Zhang, W., Zhu, C., e Yokoi, H. (2019). Automatic welding defect detection of x-ray images by using cascade adaboost with penalty term. IEEE Access, 7:125929–125938.
Fucsok, F., Muller, C., e Scharmach, M. (2002). Reliability of routine radiographic film evaluation–an extended roc study of the human factor. In 8th European Conference on Non Destructive Testing, Barcelona, June, pages 17–21.
He, K., Zhang, X., Ren, S., e Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Krizhevsky, A., Sutskever, I., e Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
Liao, T. W. (2003). Classification of welding flaw types with fuzzy expert systems. Expert Systems with Applications, 25(1):101–111.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., e Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988.
Liu, B., Zhang, X., Gao, Z., e Chen, L. (2017). Weld defect images classification with vgg16-based neural network. In International Forum on Digital TV and Wireless Multimedia Communications, pages 215–223. Springer.
Mery, D. e Arteta, C. (2017). Automatic defect recognition in x-ray testing using computer vision. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 1026–1035. IEEE.
Nacereddine, N. e Tridi, M. (2005). Computer-aided shape analysis and classification of weld defects in industrial radiography based invariant attributes and neural networks. In ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pages 88–93. IEEE.
Rana, S. et al. (2008). Facts and data on environmental risks-oil and gas drilling operations. In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers.
Ronneberger, O., Fischer, P., e Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Shorten, C. e Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60.
Simonyan, K. e Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., e Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Wang, Y., Shi, F., e Tong, X. (2019). A welding defect identification approach in x-ray images based on deep convolutional neural networks. In International Conference on Intelligent Computing, pages 53–64. Springer.
Zapata, J., Vilar, R., e Ruiz, R. (2012). Automatic inspection system of welding radiographic images based on ann under a regularisation process. Journal of Nondestructive Evaluation, 31(1):34–45.
Publicado
20/10/2020
Como Citar
SILVA, Rafael; SUYAMA, Fernando; SILVA, Ricardo; DELGADO, Myriam.
Filtragem de Dados de Treinamento para Aprendizagem Profunda Aplicada à Inspeção de Juntas Soldadas em Tubulações de Petróleo. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 686-697.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12170.
