摘要
针对轨面缺陷样本数量少、种类多的特点,以及在真实场景下存在迁移学习效果不稳定、阈值分割易受环境因素影响的问题,提出一种零样本的改进马尔可夫随机场轨面缺陷分割方法。对采集的数据使用Gabor函数进行处理,突出缺陷特征,降低数据维度,得到降维特征图;对处理后的特征图进行Kmeans聚类,缩减数据分布,降低反光和阴影的影响,并将聚类结果作为预分类矩阵;通过降维特征图和预分类矩阵构建改进马尔可夫随机场(MRF)双层图模型,并进行推理;根据模型推理出的预分类矩阵特征值来分析缺陷部分的局部几何结构;标记出缺陷区域,并完成缺陷分割。使用自采样数据集进行对比实验和消融实验,结果表明:所提方法在自采样数据集上的像素准确率、平均像素准确率、加权交并比、均交并比分别达到93.6%、80.7%、89.4%、68.2%,超过对比检测方法精度。
In view of the characteristics of the small number and many types of rail defect samples,unstable transfer learning effect in real scenes,and threshold segmentation susceptible to environmental factors,an improved Markov defect segmentation method with zero samples was proposed.Firstly,the collected data was processed using Gabor functions to highlight defect features and reduce data dimensionality,resulting in a reduced dimensionality feature map.To mitigate the effects of glare and shadows,the feature map was subjected to Kmeans clustering to reduce data distribution.The clustering results were then used as a pre-classification matrix.Based on the reduced dimensionality feature map and the pre-classification matrix,a two-layer graph model of the improved Markov random field(MRF)was constructed for inference.The model analyzed the local geometric structure of the defect area by using the eigenvalues of the classification matrix inferred by the model.Finally,the defect regions were labelled,and the defect segmentation was completed.In the experimental part,a self-sampled dataset was used to draw the final conclusion through comparative and ablation experiments.The experimental results show that the proposed method achieves pixel accuracy,mean pixel accuracy,weighted intersection over union,and mean intersection over union of 93.6%,80.7%,89.4%,and 68.2%,respectively,on the self-sampled dataset,surpassing the accuracy of other comparative detection algorithms.
作者
闵永智
刘洋
MIN Yongzhi;LIU Yang(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Department of Mechanical Egineering,Baotou Railway Vocational&Technical College,Baotou 014060,China)
出处
《北京航空航天大学学报》
北大核心
2025年第6期1863-1872,共10页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(62066024)
2023甘肃省优秀研究生“创新之星”项目(2023CXZX-615)。
关键词
图像处理
轨面缺陷
马尔可夫随机场
概率图模型
实例分割
image processing
rail surface defect
Markov random field
probabilistic graphical model
instance segmentation
