基于多模态特征的重载铁路钢轨损伤检测方法

Damage detection method of heavy duty railway rail based on multimodal features

列车重载运行的情况下钢轨会出现损伤,传统的钢轨损伤检测方法主要依靠人工巡检或使用单一模态特征进行分析,存在准确性低、易漏检漏报等问题。针对这一问题,提出了基于多模态特征的重载铁路钢轨损伤检测方法。首先采集重载铁路钢轨损伤图像,并以直方图均衡化的增强方式预处理原始图像;然后将图像中的信息转换为模态向量进行特征提取,选择置信度矩阵表示不同特征的分布情况,并分解钢轨损伤图像中的特征模态元素;最后基于多模态特征,关联具有相关性的损伤特征模量,标注损失特征标签,实现重载钢轨损伤情况的判断和检测。分别以5000,10000,30000 t重载量级的铁路钢轨作为对象进行测试,试验结果表明,所提方法能够实现精准的损伤定位,且对不同量级钢轨的损伤情况均具有较高检测精度,具有实际应用价值。

The rail will be damaged when the train is under heavy load operation.The traditional rail damage detection methods mainly rely on manual inspection or use of single modal characteristics for analysis,which has problems such as low accuracy,missing detection and reporting.Therefore,research is conducted on the detection method of rail damage in heavy-duty railways based on multimodal features.Firstly,images of rail damage in heavy-duty railways are collected,and the original images are preprocessed using histogram equalization.Then,the information in the image is transformed into modal vectors for feature extraction.The confidence matrix is selected to represent the distribution of different features in the image,and the feature modal elements in the rail damage image are decomposed.Finally,based on multimodal features,correlated damage feature moduli with correlation are annotated with loss feature labels to achieve the judgment and detection of heavy-duty rail damage.The experimental results showed that the proposed method can accurately located the damage location and had high detection accuracy for different levels of rail damage,with 5000,10000,30000 t heavy-duty railway rails as the test objects.It has practical application value.