MT-CrackNet:A multi-task deep learning framework for automatic in-situ fatigue micro-crack detection and quantification
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OpenAlex topics: Infrastructure Maintenance and Monitoring · Non-Destructive Testing Techniques · Fatigue and fracture mechanics
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References (38)
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1. Probabilistic notch fatigue assessment under size effect using micromechanics-based critical distance theory10.1016/j.ijfatigue.2024.108280 ↗
2. Characterisation of strain localisation processes during fatigue crack initiation and early crack propagation by SEM-DIC in an advanced disc alloy10.1016/j.msea.2017.05.091 ↗
3. Fractographical quantitative analysis of EN-AW 2024 aluminum alloy after creep pre-strain and LCF loading10.1016/j.engfracmech.2023.109182 ↗
4. In-situ quantitative tracking of micro-crack evolution behavior inside CMCs under load at high temperature: A deep learning method10.1016/j.actamat.2023.119073 ↗
5. Crack initiation mechanisms in Ti-6Al-4V subjected to cold dwell-fatigue, low-cycle fatigue and high-cycle fatigue loadings10.1016/j.actamat.2022.118560 ↗
6. Effect of microstructure on fatigue-crack propagation of 18CrNiMo7-6 high-strength steel10.1016/j.ijfatigue.2022.107027 ↗
7. A brief note on monotonic and fatigue fracture events investigation of thin-walled tubular austenitic steel specimens via fracture surface topography analysis (FRASTA)10.1016/j.engfailanal.2022.106048 ↗
8. An image processing approach for fatigue crack identification in cellulose acetate replicas10.1016/j.engfailanal.2024.108663 ↗
9. Fatigue damage characterization for composite laminates using deep learning and laser ultrasonic10.1016/j.compositesb.2021.108816 ↗
10. Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks10.1111/ffe.13433 ↗
11. Multi-feature vision transformer for automatic defect detection and quantification in composites using thermography10.1016/j.ndteint.2023.103033 ↗
12. Artificial neural network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters10.1016/j.ijfatigue.2023.107997 ↗
13. Research on crack detection method of wind turbine blade based on a deep learning method
14. A deep learning-based fatigue crack growth rate measurement method using mobile phones10.1016/j.ijfatigue.2022.107327 ↗
15. A deep learning-based approach for crack damage detection using strain field10.1016/j.engfracmech.2023.109703 ↗
16. Fully convolution network architecture for steel-beam crack detection in fast-stitching images10.1016/j.ymssp.2021.108377 ↗
17. Automated crack assessment and quantitative growth monitoring10.1111/mice.12626 ↗
18. Automatic concrete infrastructure crack semantic segmentation using deep learning10.1016/j.autcon.2023.104950 ↗
19. Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field10.1016/j.measurement.2020.108693 ↗
20. Designing network design strategies through gradient path analysis
21. Li R. Development of a Micro-Target Detection Algorithm Based on Refactored SPPCSPC and Enhanced YOLOv7. 2023 4th International Conference on Information Science and Education (ICISE-IE), 2023, p. 97–101. doi: 10.1109/ICISE-IE60962.2023.10456406.10.1109/ICISE-IE60962.2023.10456406 ↗
22. Woo S, Park J, Lee J-Y, Kweon IS. CBAM: Convolutional Block Attention Module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer Vision – ECCV 2018, Cham: Springer International Publishing; 2018, p. 3–19. doi: 10.1007/978-3-030-01234-2_1.10.1007/978-3-030-01234-2_1 ↗
23. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
24. Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors 2022. doi: 10.48550/ arXiv.2207.02696.10.1109/CVPR52729.2023.00721 ↗
25. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015. doi: 10.48550/ arXiv.1505.04597.
26. Ruby DAU, Theerthagiri P, Jacob DIJ, Vamsidhar DY. Binary cross entropy with deep learning technique for Image classification 2020. doi: 10.30534/ijatcse/2020/175942020.10.30534/ijatcse/2020/175942020 ↗
27. Distance-IoU loss: Faster and better learning for bounding box regression10.1609/aaai.v34i07.6999 ↗
28. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function10.1016/j.neucom.2018.05.011 ↗
29. Soomro TA, Afifi AJ, Gao J, Hellwich O, Paul M, Zheng L. Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss. 2018 Digital Image Computing: Techniques and Applications (DICTA), 2018, p. 1–8. doi: 10.1109/DICTA.2018.8615770.10.1109/DICTA.2018.8615770 ↗
30. Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s10.1038/s41598-022-17340-7 ↗
31. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
32. Sinha NK, Griscik MP. A Stochastic Approximation Method. IEEE Transactions on Systems, Man, and Cybernetics 1971; SMC-1: 338–44. doi: 10.1109/TSMC.1971.4308316.10.1109/TSMC.1971.4308316 ↗
33. Loshchilov I, Hutter F. SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv: Learning 2016. doi: 10.48550/arXiv.1608.03983.
34. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
35. U2-Net: Going deeper with nested U-structure for salient object detection10.1016/j.patcog.2020.107404 ↗
36. Pyramid scene parsing network
37. TwinLiteNet: An efficient and lightweight model for driveable area and lane segmentation in self-driving cars
38. Poudel RPK, Liwicki S, Cipolla R. Fast-SCNN: Fast Semantic Segmentation Network. ArXiv 2019. doi: 10.48550/arXiv.1902.04502.