PSI - Issue 64

Jie Wang et al. / Procedia Structural Integrity 64 (2024) 1326–1333 Author name / Structural Integrity Procedia 00 (2019) 000–000

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4. CONCLUSIONS This paper proposed a method for detecting displacement fields and invisible surface cracks no wide than 0.1 mm on steel. Two frames of images of the target under different loads was taken as input. Dense matching of feature points was adopted to evaluate the global displacement field. Afterward, discontinuity in the displacement field was extract to detect the surface crack. A case study was conducted on a steel plate with a center crack to explore the feasibility of the approach, which implied that the method could be used to detect the surface crack with reasonable accuracy. Further research is needed to investigate the accuracy of the displacement field and other potential influencing factors. Acknowledgements The authors wish to acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 52222803) and the Fundamental Research Funds for the Central Universities. References Halabe UB, Franklin R. Fatigue crack detection in metallic members using ultrasonic Rayleigh waves with time and frequency analyses[J]. Materials evaluation, 2001, 59(3): 424-431. Eonid Solovyov, Alexander Solovyov. Thermal Method in the Control of Fatigue Cracks in Welded Bridge Superstructures[J]. Transportation Research Procedia, 2021, 54: 355-361. Masahiro ICHIKAWA, Tohru TAKAMATSU, Takashi MATSUMURA. Measurement of Small Crack Lengths under Creep-Fatigue Condition by Means of Image Processing[J]. JSME international journal series 1 - Solid mechanics, 1992, 35(2): 241-246. C.Y. Hsu, B.S. Ho, L.W. Kang, et al. Fast vision-based surface inspection of defects for steel billets[C]. 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Seoul, Korea (South): IEEE, 2016. Zhang Jianjun, Luo Jing. Surface Crack edge Detection Algorithm based on improved Sobel operator [J]. Journal of Hefei University of Technology (Natural Science Edition), 2011,34 (06):845-847. Yeum, C.M., Dyke, S.J. Vision-Based Automated Crack Detection for Bridge Inspection[J]. Computer-Aided Civil and Infrastructure Engineering, 2015, 30: 759-770. Ortiz A, Bonnin-Pascual F, Garcia-Fidalgo E, et, al. Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application[J]. Sensors, 2016, 16(12): 2118. Qinghua Han, Xuan Liu, Jie Xu. Detection and Location of Steel Structure Surface Cracks Based on Unmanned Aerial Vehicle Images[J]. Journal of Building Engineering, 2022, 50: 104098. Al-Salih, H, Juno, M, Collins, et al. Application of a digital image correlation bridge inspection methodology on geometrically complex bifurcated distortion-induced fatigue cracking[J]. Fatigue Fract Eng Mater Struct, 2021, 44(11): 3186–3201. Kong, X., Li, J. Vision-Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33: 783-799. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60: 91–110. J. Sun, Z. Shen, Y. Wang, et al. LoFTR: Detector-Free Local Feature Matching with Transformers[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA: IEEE, 2021.