Application of vibration compensation based on image processing in track displacement monitoring
Affiliation:
1. School of Information Engineering, Yancheng Teachers University , Yancheng , Jiangsu, 224002 , China
Abstract
Abstract
The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.
Publisher
Walter de Gruyter GmbH
Subject
Behavioral Neuroscience,Artificial Intelligence,Cognitive Neuroscience,Developmental Neuroscience,Human-Computer Interaction
Reference31 articles.
1. M. Subhani, J. Li, B. Samali, and K. Crews, “Reducing the effect of wave dispersion in a timber pole based on transversely isotropic material modelling,” Constr. & Build. Mater., vol. 102, no. JAN.15PT.2, pp. 985–998, 2016. 2. Z. Yang, H. Mei, X. Sun, and P. Jia, “Compensation control of rotor mass eccentric vibration for bearingless induction motors,” J. Power Electron., vol. 21, no. 5, pp. 792–803, 2021. 3. Q. Liu, J. Wang, L. Xiao, L. I. Jichao, B. Liu, and X. Zhang, “Application of OFDR-based sensing technology in geo-mechanical model test on tunnel excavation using cross rock pillar method,” Yanshilixue Yu Gongcheng Xuebao/Chinese J. Rock. Mech. Eng., vol. 36, no. 5, pp. 1063–1075, 2017. 4. H. Wang, J. Fu, W. Lin, S. Hu, C. J. Kuo, and L. Zuo, “Image quality assessment based on local linear information and distortion-specific compensation,” IEEE Trans. Image Process, vol. 26, no. 2, pp. 915–926, 2017. 5. Y. Wang, S. Feng, and Q. Zhang, “Inverse synthetic aperture radar imaging of multi-targets based on image processing technique,” J. Harbin Inst. Technol. (New Ser.), vol. 25, no. 6, pp. 46–58, 2018.
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