摘要
基于卷积神经网络的深度学习方法对钢轨表面损伤的自动化检测起到非常重要的推动作用,因此提出一种基于卷积神经网络的钢轨表面损伤检测方法。首先,在经典U-Net的收缩路径和扩展路径之间增加一个分支网络,可以辅助U-Net输出理想的分割图。然后,将Type-I RSDDs高速铁路轨道数据集作为检测样本,使用数据增强的手段扩增检测样本后馈入改进的U-Net中进行训练和测试。最后,采用评价指标对所提方法进行评估。实验结果表明,所提方法的检测准确率达到99.76%,相比于其他方法的最高水平提高6.74个百分点,说明所提方法可以显著提高检测准确率。
The deep learning method based on convolutional neural network plays a very important role in promoting the automatic detection of rail surface damage.Therefore,a method based on convolutional neural network for rail surface damage detection is proposed.First,a branch network is added between the contraction path and extension path of the classic U-Net can assist U-Net to output the ideal segmentation graph.Then,the type-I RSDDs high-speed railway track dataset is taken as the test sample,and the test sample is amplified by means of data enhancement and fed into the improved U-Net for training and testing.Finally,the evaluation index is used to evaluate the proposed method.The experimental results show that the detection accuracy of the proposed method reaches 99.76%,which is 6.74 percentage higher than the highest level of other methods,indicating that the proposed method can significantly improve the detection accuracy.
作者
梁波
卢军
曹阳
Liang Bo;Lu Jun;Cao Yang(College of Mechanical&Electrical Engineering,Shaanxi University of Science and Technology,Xi'an,Shaanxi 710021,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第2期326-332,共7页
Laser & Optoelectronics Progress
基金
陕西省科技厅自然科学基金(2016GY-049)。
关键词
机器视觉
深度学习
损伤特征识别
数据增强
改进的U-Net图形分割网络
无损检测
machine vision
deep learning
damage feature recognition
data enhancement
improved U-Net image segmentation network
non-destructive detection
