Abstract
Computer-aided weld defect recognition is transforming the field of Non-Destructive Testing by addressing the shortcomings of slow and error-prone manual inspections. This technology provides a reliable solution for detecting changes in pipeline conditions and structural damage. While conventional neural networks fall short in precise fault localization, deep learning-based object detection techniques step in to fill the gap. Addressing a real-industrial problem, particularly visually inspecting an X-ray welding database, without relying on a pre-existing benchmark presents a significant challenge in this field. Additionally, the poor quality of our welding data, which is riddled with small, sticky porosity in each image, poses several issues related to selecting the appropriate deep neural network object detector. This is yet another challenge that needs to be tackled. To direct these challenges, we introduced a novel approach based on the renowned Faster RCNN architecture to develop a model specifically designed for weld defect detection and recognition. This study dives deep into the inner workings of this newly adopted methodology. In our research, we have thoroughly parameterized, trained, tested, and validated this model. Our approach stands out through a comparative analysis with YOLO and DCNN models, highlighting the superiority of our Faster RCNN-based system. By evaluating its robustness and efficiency, our study reveals that the Faster RCNN model outperforms its counterparts in weld defect detection and localization for this specific small and sticky porosity defect type. This stands as a testament to effectively setting a new standard in this area.
Similar content being viewed by others
Data Availability
It is not applicable.
Notes
“Objectness” estimates membership in a group of object classes with respect to the background.
References
Felzenszwalb, P.F., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Machine Intell. 32(9), 1627–1645 (2009)
Sung, K.-K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Machine Intell. 20(1), 39–51 (1998)
Dollar, P., et al.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Machine Intell. 34(4), 743–761 (2011)
Kobatake, H., Yoshinaga, Y.: Detection of spicules on mammogram based on skeleton analysis. IEEE Trans. Med. Imaging 15(3), 235–245 (1996)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia (2014)
Cao, Z., et al.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Yang, Z., and Nevatia, R.: A multi-scale cascade fully convolutional network face detector. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)
Chen, C., et al.: Deep driving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks (2016). arXiv:1506.01497.
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2017)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Boaretto, N., Centeno, T.M.: Automated detection of welding defects in pipelines from radiographic images DWDI. NDT & E Int. 86, 7–13 (2017)
Bastian, B.T., et al.: Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDT & E Int. 107, 102134 (2019)
Shin, H.-C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Szegedy, Christian, et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Girshick, R, et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Szegedy, C., Erhan, D.: Deep neural networks for object detection. Adv. Neural Inf. Process. Syst. 26 (2013)
Erhan, D., et al.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Szegedy, C., et al.: Scalable, high-quality object detection (2014). arXiv:1412.1441
Sermanet, P., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks (2013). arXiv:1312.6229
Sa, R., et al.: Fast scale-invariant lateral lumbar vertebrae detection and segmentation in X-ray images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2016)
Hou, W., et al.: Review on computer aided weld defect detection from radiography images. Appl. Sci. 10(5), 1878 (2020)
Asban, H., et al.: Welding defect detection from radiography images with a cepstral approach. NDT & E Int. 44(2), 226–231 (2011)
Mery, D., Berti, M.A.: Automatic detection of welding defects using texture features. Insight Nondestruct. Test. Cond. Monit. 45(10), 676–681 (2003)
Valavanis, I., Kosmopoulos, D.: Multiclass defect detection and classification in weld radiographic images using geometric and texture features. Expert Syst. Appl. 37(12), 7606–7614 (2010)
Wang, G., Liao, T.W.: Automatic identification of different types of welding defects in radiographic images. NDT & E Int. 35(8), 519–528 (2002)
Kumar, J., Anand, R.S., Srivastava, S.P.: Flaws classification using ANN for radiographic weld images. In: 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE (2014)
Zapata, J., Vilar, R., Ruiz, R.: Automatic inspection system of welding radiographic images based on ANN under a regularisation process. J. Nondestruct. Eval. 31(1), 34–45 (2012)
Khalifa, W., et al.: Classification of welding defects using gray level histogram techniques via neural network. Mansoura Eng. J. 39(4), 1–13 (2020)
Vilar, R., Zapata, J., Ruiz, R.: An automatic system of classification of weld defects in radiographic images. NDT & E Int. 42(5), 467–476 (2009)
Wang, X., Wong, B.S., Tan, C.-S.: Recognition of welding defects in radiographic images by using support vector machine classifier. Res. J. Appl. Sci. Eng. Technol. 2(3), 295–301 (2010)
Mu, W., et al.: Automatic classification approach to weld defects based on PCA and SVM. Insight-Non-Destruct. Test. Cond. Monit. 55(10), 535–539 (2013)
Baniukiewicz, P.: Automated defect recognition and identification in digital radiography. J. Nondestruct. Eval. 33(3), 327–334 (2014)
Chen, B., et al.: Accurate defect detection via sparsity reconstruction for weld radiographs. NDT and E Int. 94, 62–69 (2018)
Ajmi, C., et al.: Deep learning technology for weld defects classification based on transfer learning and activation features. Adv. Mater. Sci. Eng. (2020). https://doi.org/10.1155/2020/1574350
Nowroth, C., et al.: Deep learning-based weld contour and defect detection from micrographs of laser beam welded semi-finished products. Appl. Sci. 12(9), 4645 (2022)
Zhu, H., Ge, W., Liu, Z.: Deep learning-based classification of weld surface defects. Appl. Sci. 9(6), 3312 (2019)
Cha, Y.-J., et al.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. Aided Civil Infrastruct. Eng. 33(9), 731–747 (2018)
Lin, W.-Y., et al.: Steel surface defects detection based on deep learning. In: Advances in Physical Ergonomics & Human Factors: Proceedings of the AHFE 2018 International Conference on Physical Ergonomics & Human Factors, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA 9. Springer, Berlin (2019)
Wen, G.U.O., Kai, L.I.U., Hui-fan, Q.U.: Welding defect detection of X-ray images based on faster R-CNN model. J. Beijing Univ. Posts Telecommun. 42(6), 20 (2019)
Oh, S., et al.: Automatic detection of welding defects using faster R-CNN. Appl. Sci. 10(23), 8629 (2020)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Ajmi, C.: Classification of welding defects: an approach based on deep learning for computer vision (2021).
Mery, D., et al.: GDXray: the database of X-ray images for nondestructive testing. J. Nondestruct. Eval. 34(4), 1–12 (2015)
Ajmi, C., El Ferchichi, S., Laabidi, K.: New procedure for weld defect detection based-gabor filter. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC ASET). IEEE (2018)
Acknowledgements
This work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE).
Funding
It is not applicable.
Author information
Authors and Affiliations
Contributions
The primary contributor to this manuscript, including the writing, results, and innovation, is the corresponding author. The remaining authors were involved in a minor manuscript revision.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical Approval
It is not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ajmi, C., Zapata, J., Elferchichi, S. et al. Advanced Faster-RCNN Model for Automated Recognition and Detection of Weld Defects on Limited X-Ray Image Dataset. J Nondestruct Eval 43, 14 (2024). https://doi.org/10.1007/s10921-023-01032-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10921-023-01032-x