基于活动轮廓模型的在线铁谱图像分割研究
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国家自然科学基金青年基金项目(No.11901071);经济社会应用统计重庆市重点实验室项目(No.KFJJ2022022)


Research on Online Iron Ferrography Image Segmentation Based on Active Contour Model
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    摘要:

    为了准确分割磨粒区域以得到油液中铁谱磨粒的含量,从而获取设备油品及故障信息,以图像可视在线铁谱传感器获取的磨粒图像为对象,引入了基于曲线演化理论和水平集方法的两个区域几何轮廓模型,即LBF模型及IR模型。通过对比这2种模型的分割效果发现:IR模型具有更高的分割准确率和更快的收敛速度。然后进一步分析了模型参数对分割结果的影响,得出不同磨粒浓度条件下对应最优分割效果和最短运算时间的分割参数值,为在线监测中磨粒图像分割参数的自适应选择提供了依据。实验结果表明,研究所采用的分割模型对于磨粒图像具有更高的分割准确率和收敛速度,为后续快速、准确计算油液中铁谱磨粒含量提供了保证。

    Abstract:

    In order to segment the wear particle area accurately for calculating the ferrographic abrasive content in the oil, and finally obtain equipment oil products and fault information, taking the wear particle image of the image-visual online ferrography sensor as the object, the region-based geometric active contour models based on curve evolution theory and level set method: LBF model and IR model were introduced. By comparing the segmentation effects of two models, it is found that the IR model has higher segmentation accuracy and faster convergence speed. The influence of model parameters on the segmentation results was further analyzed, and the segmentation parameter values corresponding to the optimal segmentation effect and the shortest computing time under different wear particle concentration conditions were obtained, which provided a basis for the adaptive selection of wear particle image segmentation parameters in online monitoring. The experiment shows that the segmentation model adopted has higher segmentation accuracy and convergence speed for the wear particle image, which provides a guarantee for the subsequent fast and accurate calculation of the ferrographic wear particle content in the oil.

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许晓曾,朱维莉,王艳.基于活动轮廓模型的在线铁谱图像分割研究[J].重庆师范大学学报自然科学版,2023,40(4):122-128

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  • 在线发布日期: 2023-09-22