journal article·2024-03

Approach for Automatic Defect Detection in Aluminum Casting X-Ray Images Using Deep Learning and Gain-Adaptive Multi-Scale Retinex

Chao Hai, Yapeng Wu, Hong Zhang, Fanyong Meng, Dalong Tan, Min Yang

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OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced X-ray and CT Imaging · Welding Techniques and Residual Stresses

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References (42)

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1. Riaz, F., Kamal, K., Zafar, T., Qayyum, R.: An inspection approach for casting defects detection using image segmentation. In: 2017 International Conference on Mechanical, System and Control Engineering (ICMSC) (2017). https://doi.org/10.1109/icmsc.2017.795945110.1109/icmsc.2017.7959451 ↗
2. Ingle, V., Sorte, M.: Defects, root causes in casting process and their remedies: review. Int. J. Eng. Res. Appl. 07, 47–54 (2017). https://doi.org/10.9790/9622-070303475410.9790/9622-0703034754 ↗
3. Fiorese, E., Llo, F.B., Timelli, G., et al.: New classification of defects and imperfections for aluminum alloy castings. Int. J. Metalcast. 9(1), 55–66 (2015). https://doi.org/10.1007/bf0335560210.1007/bf03355602 ↗
4. Dwivedi, S.K., Vishwakarma, M., Soni, P.A.: Advances and researches on non destructive testing: a review. Mater. Today: Proc. 5(2), 3690–3698 (2018). https://doi.org/10.1016/j.matpr.2017.11.62010.1016/j.matpr.2017.11.620 ↗
5. Strecker, H.: A local feature method for the detection of flaws in automated x-ray inspection of castings. Signal Process. 5(5), 423–431 (1983). https://doi.org/10.1016/0165-1684(83)90005-110.1016/0165-1684(83)90005-1 ↗
6. Rebuffel, V., Sood, S., Blakeley, B.: Defect detection method in digital radiography for porosity in magnesium castings. Materials Evaluation, ECNDT (2006)
7. Zhao, X., Liang, J., He, Z., et al.: Blowhole detection based on bidirectional enhancement and omnidirectional analysis for X-ray inspection of castings. Math. Probl. Eng. (2019). https://doi.org/10.1155/2019/246850510.1155/2019/2468505 ↗
8. Mery, D.: Automated detection in complex objects using a tracking algorithm in multiple X-ray views. CVPR 2011 Workshops (2011). https://doi.org/10.1109/cvprw.2011.598171510.1109/cvprw.2011.5981715 ↗
9. Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robot. Autom. 18(6), 890–901 (2002). https://doi.org/10.1109/tra.2002.80564610.1109/tra.2002.805646 ↗
10. Zou, Y., Du, D., Chang, B., et al.: Automatic weld defect detection method based on Kalman filtering for real-time radiographic inspection of spiral pipe. NDT E Int. 72, 1–9 (2015). https://doi.org/10.1016/j.ndteint.2015.01.00210.1016/j.ndteint.2015.01.002 ↗
11. Lecomte, G., Kaftandjian, V., Cendre, E., et al.: A robust segmentation approach based on analysis of features for defect detection in X-ray images of aluminium castings. Insight 49(10), 572–577 (2007). https://doi.org/10.1784/insi.2007.49.10.57210.1784/insi.2007.49.10.572 ↗
12. Tang, Y., Zhang, X., Li, X., et al.: Application of a new image segmentation method to detection of defects in castings. Int. J. Adv. Manuf. Technol. 43(5–6), 431–439 (2008). https://doi.org/10.1007/s00170-008-1720-110.1007/s00170-008-1720-1 ↗
13. Li, W., Li, K., Huang, Y., et al.: A new trend peak algorithm with X-ray image for wheel hubs detection and recognition. In: Computational Intelligence and Intelligent Systems(2016). https://doi.org/10.1007/978-981-10-0356-1_310.1007/978-981-10-0356-1_3 ↗
14. Cogranne, R., Retraint, F.: Statistical detection of defects in radiographic images using an adaptive parametric model. Signal Process. 96, 173–189 (2014). https://doi.org/10.1016/j.sigpro.2013.09.01610.1016/j.sigpro.2013.09.016 ↗
15. Zhao, X., He, Z., Zhang, S.: Defect detection of castings in radiography images using a robust statistical feature. J. Opt. Soc. Am. A 31(1), 196 (2013). https://doi.org/10.1364/josaa.31.00019610.1364/josaa.31.000196 ↗
16. Fillatre, L., Nikiforov, I., Retraint, F.: ε-Optimal non-Bayesian anomaly detection for parametric tomography. IEEE Trans. Image Process. 17(November (11)), 1985–1999 (2008). https://doi.org/10.1109/TIP.2008.200443110.1109/TIP.2008.2004431 ↗
17. Bengio, Y.: Learning Deep Architectures for AI. Now Publishers Inc, Hanover (2009)10.1561/9781601982957 ↗
18. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/306538610.1145/3065386 ↗
19. Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conf. Comput. Vis. Pattern Recognit. 2014, 580–587 (2014). https://doi.org/10.1109/cvpr.2014.8110.1109/cvpr.2014.81 ↗
20. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/tpami.2016.257703110.1109/tpami.2016.2577031 ↗
21. Liu, W., Anguelov, D., Erhan, D., et al.: SSD: single shot multibox detector. Lecture Notes in Computer Science, pp. 21–37 (2016). https://doi.org/10.1007/978-3-319-46448-0_210.1007/978-3-319-46448-0_2 ↗
22. Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.1109/cvpr.2016.9110.1109/cvpr.2016.91 ↗
23. Jiang, L., Wang, Y., Tang, Z., et al.: Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation. Measurement (2021). https://doi.org/10.1016/j.measurement.2020.10873610.1016/j.measurement.2020.108736 ↗
24. Hou, W., Wei, Y., Jin, Y., et al.: Deep features based on a DCNN model for classifying imbalanced weld flaw types. Measurement 131, 482–489 (2019). https://doi.org/10.1016/j.measurement.2018.09.01110.1016/j.measurement.2018.09.011 ↗
25. Wang, S., Xia, X., Ye, L., et al.: Automatic detection and classification of steel surface defect using deep convolutional neural networks. Metals 11(3), 388 (2021). https://doi.org/10.3390/met1103038810.3390/met11030388 ↗
26. Mery, D.: Aluminum casting inspection using deep learning: a method based on convolutional neural networks. J. Nondestruct. Eval. (2020). https://doi.org/10.1007/s10921-020-0655-910.1007/s10921-020-0655-9 ↗
27. Ferguson, M., Ak, R., Lee, Y.T., et al.: Automatic localization of casting defects with convolutional neural networks. In: 2017 IEEE International Conference on Big Data (Big Data) (2017). https://doi.org/10.1109/bigdata.2017.825811510.1109/bigdata.2017.8258115 ↗
28. Ferguson, M., Ak, R., Lee, Y.T., et al.: Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain. Manuf. Syst. 2(1), 20180033 (2018). https://doi.org/10.1520/ssms2018003310.1520/ssms20180033 ↗
29. Uijlings, J.R.R., Van De Sande, K.E.A., Gevers, T., et al.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-510.1007/s11263-013-0620-5 ↗
30. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.1109/cvpr.2016.9010.1109/cvpr.2016.90 ↗
31. Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y10.1007/s11263-015-0816-y ↗
32. Xu, X., Lei, Y., Yang, F.: Railway subgrade defect automatic recognition method based on improved Faster R-CNN. Sci. Program. (2018). https://doi.org/10.1155/2018/483297210.1155/2018/4832972 ↗
33. Bai, T., Yang, J., Xu, G., et al.: An optimized railway fastener detection method based on modified Faster R-CNN. Measurement 182, 109742 (2021). https://doi.org/10.1016/j.measurement.2021.10974210.1016/j.measurement.2021.109742 ↗
34. Lei, X., Sui, Z.: Intelligent fault detection of high voltage line based on the Faster R-CNN. Measurement 138, 379–385 (2019). https://doi.org/10.1016/j.measurement.2019.01.07210.1016/j.measurement.2019.01.072 ↗
35. Liao, S., Piao, Y., Li, B.: Low illumination color image enhancement based on improved Retinex. LIDAR imaging detection and target recognition 2017. SPIE 10605, 811–819 (2017)
36. Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 936–944 (2017). https://doi.org/10.1109/cvpr.2017.10610.1109/cvpr.2017.106 ↗
37. He, K., Gkioxari, G., Dollár, P., et al.: Mask R-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2980–2988 (2017). https://doi.org/10.1109/ICCV.2017.32210.1109/ICCV.2017.322 ↗
38. Tzutalin. LabelImg. Git code. https://github.com/tzutalin/labelImg (2015)
39. Agaian, S.S., Silver, B., Panetta, K.A.: Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16(3), 741–758 (2007)10.1109/TIP.2006.888338 ↗
40. Raju, G., Nair, M.S.: A fast and efficient color image enhancement method based on fuzzy-logic and histogram. AEU Int. J. Electron. Commun. 68(3), 237–243 (2014)10.1016/j.aeue.2013.08.015 ↗
41. Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016). https://doi.org/10.1109/TIP.2016.263945010.1109/TIP.2016.2639450 ↗
42. Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https://doi.org/10.1109/tpami.2018.285882610.1109/tpami.2018.2858826 ↗