• Spectroscopy and Spectral Analysis
  • Vol. 39, Issue 5, 1637 (2019)
SHAO Lei, ZHANG Yi-ming, LI Ji, LIU Hong-li, CHEN Xiao-qi, and YU Xiao
Author Affiliations
  • [in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2019)05-1637-06 Cite this Article
    SHAO Lei, ZHANG Yi-ming, LI Ji, LIU Hong-li, CHEN Xiao-qi, YU Xiao. Research on High Temperature Region Segmentation of Infrared Pipeline Image Based on Improved Two-Dimensional-Otsu[J]. Spectroscopy and Spectral Analysis, 2019, 39(5): 1637 Copy Citation Text show less

    Abstract

    The petrochemical pipeline can usually be divided into the normal temperature region and the high temperature region. The existence of high temperature region affects the safe operation of the whole system, and the loss of heat will cause a series of problems, such as the waste of resources and the pollution of the environment. For the realization of quickly and accurately separating the high temperature region from the infrared image, based on the basic one-dimensional Otsu algorithm we propose an improved two-dimensional multi threshold method. First, the algorithm divides the infrared image of pipeline into two parts: background and pipeline through classical single threshold segmentation. Then, based on the image region of the pipeline, the two thresholds of the target image are divided by the two dimensional image of the pipeline gray image and the average value image, and the larger threshold is finally taken as the segmentation point of the normal temperature region and the high temperature region. We analyze the different pipeline for several tests. The results show that the improved two-dimensional Otsu threshold algorithm can extract the pipeline from complex background more clearly, and on the basis of the step segment the high temperature region more accurately.
    SHAO Lei, ZHANG Yi-ming, LI Ji, LIU Hong-li, CHEN Xiao-qi, YU Xiao. Research on High Temperature Region Segmentation of Infrared Pipeline Image Based on Improved Two-Dimensional-Otsu[J]. Spectroscopy and Spectral Analysis, 2019, 39(5): 1637
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