Automated pixel-level detection method of weld defects using lightweight network on X-ray images
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Abstract
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Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Advanced X-ray and CT Imaging · Industrial Vision Systems and Defect Detection
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References (70)
Show source-provided references
1. Numerical analysis and experimental research on detection of welding defects in pipelines based on magnetic flux leakage10.1016/j.ptlrs.2023.05.013 ↗
2. Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion10.1016/j.ymssp.2023.110484 ↗
3. Additively manufactured integrated slit mask for laser ultrasonic guided wave inspection10.1007/s00170-020-05946-y ↗
4. Research on pulsed eddy current automatic detection technology for weld defects of pressure vessel cylinder10.1016/j.measurement.2021.109081 ↗
5. Simulation-assisted AI for the evaluation of thermal barrier coatings using pulsed infrared thermography
6. Automated defect recognition on X-ray radiographs of solid propellant using deep learning based on convolutional neural networks10.1007/s10921-021-00750-4 ↗
7. Segmentation of x-ray image for welding defects detection using an improved chan-vese model10.1016/j.matpr.2020.12.806 ↗
8. Semi-supervised transfer learning-based automatic weld defect detection and visual inspection
9. An effective data enhancement method of deep learning for small weld data defect identification10.1016/j.measurement.2022.112245 ↗
10. Fast segmentation method for defects detection in radiographic images of welds
11. A threshold selection method from gray-level histograms10.1109/TSMC.1979.4310076 ↗
12. Research on steel barrel flattened seam recognition based on machine vision10.1088/1742-6596/1633/1/012014 ↗
13. Feature extraction of laser welding pool image and application in welding quality identification10.1109/ACCESS.2021.3108462 ↗
14. Contour estimation in synthetic and real weld defect images based on maximum likelihood
15. Image processing for non-destructive testing10.1080/09507119009447695 ↗
16. Segmentation of welding defects using a robust algorithm
17. Flaw detection in radiographic weldment images using morphological watershed segmentation technique10.1016/j.ndteint.2008.06.005 ↗
18. Flaw detection in radiographic weld images using morphological approach10.1016/j.ndteint.2005.05.005 ↗
19. Defect detection in X-ray images using fuzzy reasoning10.1016/S0262-8856(00)00075-5 ↗
20. Application of artificial neural network to discrimination of defect type in automatic radiographic testing of welds10.2355/isijinternational.39.1081 ↗
21. Development of an automatic weld defect identification system for radiographic testing
22. Pattern recognition of weld defects detected by radiographic test10.1016/j.ndteint.2003.12.004 ↗
23. Welding defect detection and classification using geometric features
24. Automatic detection of welding defects using texture features10.1784/insi.45.10.676.52952 ↗
25. Flaws classification using ANN for radiographic weld images
26. Multi - class welding flaws classification using texture feature for radiographic images
27. Detection of line weld defects based on multiple thresholds and support vector machine10.1016/j.ndteint.2008.05.004 ↗
28. Automatic defect identification technology of digital image of pipeline weld10.1016/j.ngib.2019.01.016 ↗
29. Multiclass defect detection and classification in weld radiographic images using geometric and texture features10.1016/j.eswa.2010.04.082 ↗
30. Automatic inspection system of welding radiographic images based on ANN under a regularisation process10.1007/s10921-011-0118-4 ↗
31. Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers10.1016/j.eswa.2011.01.092 ↗
32. Evaluation of the relevant characteristic parameters of welding defects and probability of correct classification using linear classifiers
33. Welding defect detection from radiography images with a cepstral approach10.1016/j.ndteint.2010.10.005 ↗
34. Automatic weld defect identification from radiographic images10.1016/j.ndteint.2012.11.005 ↗
35. Automated detection of welding defects in pipelines from radiographic images DWDI10.1016/j.ndteint.2016.11.003 ↗
36. Spectral processing technique based on feature selection and artificial neural networks for arc-welding quality monitoring10.1016/j.ndteint.2008.07.004 ↗
37. Improving the accuracy of computer-aided radiographic weld inspection by feature selection10.1016/j.ndteint.2008.11.002 ↗
38. Deep features based on a DCNN model for classifying imbalanced weld flaw types10.1016/j.measurement.2018.09.011 ↗
39. A defect recognition method for low-quality weld image based on consistent multiscale feature mapping
40. Deep learning technology for weld defects classification based on transfer learning and activation features10.1155/2020/1574350 ↗
41. Transfer learning with CNN for classification of weld defect10.1109/ACCESS.2021.3093487 ↗
42. An expert knowledge-empowered CNN approach for welding radiographic image recognition10.1016/j.aei.2023.101963 ↗
43. Welding defect detection of X-Ray images based on faster R-CNN model
44. Defects detection in weld joints based on visual attention and deep learning10.1016/j.ndteint.2022.102764 ↗
45. Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT10.1016/j.jmapro.2023.05.058 ↗
46. Generalized weld bead region of interest localization and improved faster R-CNN for weld defect recognition10.1016/j.measurement.2023.113619 ↗
47. Weld image detection and recognition based on improved YOLOv4
48. Context and scale-aware YOLO for welding defect detection10.1016/j.ndteint.2023.102919 ↗
49. Automatic detection of welding defects using faster R-CNN10.3390/app10238629 ↗
50. Defect detection in welding radiographic images based on semantic segmentation methods10.1016/j.measurement.2021.110569 ↗
51. Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning10.1520/SSMS20180033 ↗
52. Welding quality detection of metro train body based on ABC mask R-CNN10.1016/j.measurement.2023.112969 ↗
53. A nondestructive automatic defect detection method with pixelwise segmentation10.1016/j.knosys.2022.108338 ↗
54. An automatic welding defect location algorithm based on deep learning10.1016/j.ndteint.2021.102435 ↗
55. A pixel-level deep segmentation network for automatic defect detection10.1016/j.eswa.2022.119388 ↗
56. A multiple scale spaces empowered approach for welding radiographic image defect segmentation10.1016/j.ndteint.2023.102934 ↗
57. Girshick · Fast r-cnn · 2015 · 1440
58. Bochkovskiy · 2020
59. A comprehensive review of yolo architectures in computer vision: from yolov1 to yolov8 and yolo-nas10.3390/make5040083 ↗
60. U-Net: Convolutional networks for biomedical image segmentation
61. Attention is all you need
62. Wang X, Girshick R, Gupta A, He K. Non-local neural networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition; p. 7794-7803.10.1109/CVPR.2018.00813 ↗
63. Cao Y, Xu J, Lin S, Wei F, Hu H. GCNet: non-Local networks meet squeeze-excitation networks and beyond. In: 2019 IEEE/CVF international conference on computer vision workshop (ICCVW); p. 1971-1980.10.1109/ICCVW.2019.00246 ↗
64. GDXray: the database of X-ray images for nondestructive testing10.1007/s10921-015-0315-7 ↗
65. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR) p. 2881-2890.10.1109/CVPR.2017.660 ↗
66. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR); p. 2117-2125.10.1109/CVPR.2017.106 ↗
67. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support; p. 3-11.10.1007/978-3-030-00889-5_1 ↗
68. Rethinking atrous convolution for semantic image segmentation
69. Pyramid attention network for semantic segmentation
70. MA-Net: a multi-scale attention network for liver and tumor segmentation10.1109/ACCESS.2020.3025372 ↗