Abstract
Aiming at the problem of large recognition errors in traditional metal material pitting defect recognition methods, this research aims to improve the recognition performance of metal material pitting defects, and proposes a metal material pitting defect recognition method based on visual signal processing. The rail material parameters are set according to the schematic diagram of the U71Mn rail imitation, and the defective metal material imitation model is constructed according to the distribution position of each crack of the U71Mn rail, and then the optical signal of the metal material pitting defect is collected according to the geometric model imaged by the camera. The image of metal material pitting defects undergoes visual signal processing to extract the details of the image in different directions and scales. Based on this, the Mallet algorithm is used to decompose the image of metal material pitting corrosion defects, and the low-frequency components and high-dimensional components of multiple resolutions and multiple features are obtained, and specific fusion rules are selected to select the components of each layer obtained by decomposition, and then wavelet Inverse transformation, complete the fusion and analysis of visual signals of pitting defects in metal materials. Experimental results show that the recognition method of metal material pitting defects based on visual signal processing has high recognition performance.
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Zhao, Y., Zhang, L. (2021). Recognition Method of Metal Material Pitting Defect Based on Visual Signal Processing. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_2
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DOI: https://doi.org/10.1007/978-3-030-82562-1_2
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