改进的U-Net算法在管道内焊缝缺陷图像分割中的应用

Application of improved U-Net algorithm in image segmentation of pipeline inner weld defect

【目的】图像处理技术在管道焊缝识别系统中的应用已经成为了机器视觉在焊缝检测中主要应用方向。对焊缝表面缺陷进行识别是应用的关键技术。为了提高焊缝表面缺陷识别效果,需要对焊缝图像进行有效分割。针对管道内焊缝边界区域可能出现的模糊不清,导致分割结果不准确的现象,需要采取相应的技术有段进行改善。【方法】针对管道内焊缝缺陷图像分割问题,提出一种改进的U-Net图像分割方法。以管道内焊缝图像为研究对象,采用改进型U-Net网络对管道内焊缝缺陷图像进行识别和分割,经过网络训练和模型测试后,将分割结果与原U-Net网络、FCN网络进行对比。【结果】结果表明,在改进型U-Net网络对管道内焊缝缺陷图像的分割中,相似性系数(Dice)、平均交并比(mIoU)两项评价指标分别达到0.8420和0.8514,相较于FCN网络分别提升13.44%和8.68%,相较于原U-Net网络分别提升6.51%和3.31%。【结论】因此,该文提出的改进后的U-Net网络对管道内焊缝缺陷的识别和分割具有更好的效果,也为研究管道焊缝缺陷识别系统提供可靠基础,减少人工检测的成本和时间。

[Objective]Application of image processing technology in pipeline’s weld recognition system has become main application direction of machine vision in weld detection.Identification of surface defects on welds is a key technology for the application.In order to improve recognition effect of surface defects on weld,it is necessary to effectively segment weld images.In response to possible blurriness of weld boundary area inside pipeline,which leads to inaccurate segmentation results,corresponding techniques need to be adopted to improve it.[Methods]An improved U-Net image segmentation method was proposed to solve the problem of image segmentation of pipeline inner weld defects.Taking images of weld inside pipelines as the research object,the improved U-Net network was used to recognize and segment defect images of weld inside pipelines.After network training and model testing,segmentation results were compared with original U-Net network and FCN network.[Results]The results showed that the two evaluation indexes of similarity coefficient(Dice)and mean intersection over union(mIoU)of the improved U-Net network in the segmentation of weld defect images inside pipelines reached 0.8420 and 0.8514 respectively.13.44%and 8.68%were improved respectively compared with FCN network,and 6.51%and 3.31%were increased compared with original U-Net network.[Conclusion]Therefore,the improved U-Net network proposed in this paper had a better effect on identification and segmentation of pipeline’s weld defects,and also provided a reliable basis for the study of pipeline’s weld defect identification system,reducing cost and time of manual detection.