摘要
铁路线轨道道床上准确监测异物对列车安全行驶具有重要意义。基于深度学习重构的无监督异常检测算法可以解决异常数据不足对检测有影响的问题,但编码器“泛化”能力过于强大,能够很好地重建异常样本,影响其检测精度。针对此问题,提出一种基于图像修复的无砟轨道道床的异常检测算法。利用修复思想对图像进行掩码,利用不完整的非异常图像训练来对图像进行修复重建,以此来提高模型对其上下文的语义理解,增强模型的重建能力。在测试时,采用测试图像与重建图像在多尺度下的平均异常图最大值作为重构误差来计算异常分数,扩大异常图像与正常图像的重构误差的界限。实验结果表明,所提算法在MNIST、CIFAR-10公开数据集及无砟轨道道床数据集上的性能均优于其他方法。
Accurate detection of foreign objects on ballastless railway track beds is crucial for ensuring train safety.Although unsupervised anomaly detection algorithms based on deep learning address the effect of insufficient abnormal data on detection,the“generalization”ability of the encoder can proficiently reconstruct anomalous instances,thereby affecting detection accuracy.To solve this problem,this study proposes an anomaly detection framework for ballastless track beds utilizing image inpainting.First,inpainting was employed to obscure and subsequently restore the image using training on nonanomalous and incomplete image data,aiming to improve the model’s contextual semantic understanding and enhance its reconstruction ability.Second,the maximum value obtained from the average anomaly map of the test and reconstructed images,which was analyzed across multiple scales,was utilized as the reconstruction error to calculate the anomaly score.This step aimed to widen the reconstruction error boundary between the abnormal and normal images.Finally,experimental results show a notable advantage of the proposed algorithm over alternative methods on public datasets,such as MNIST,CIFAR-10,and the ballastless track bed dataset.
作者
蒋婉
杨凯
邱春蓉
谢利明
Jiang Wan;Yang Kai;Qiu Chunrong;Xie Liming(School of Physical Science and Technology,Southwest Jiaotong University,Chengdu 610031,Sichuan,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2024年第12期400-411,共12页
Laser & Optoelectronics Progress
基金
自然基金重点国际(地区)合作与交流项目(61960206010)。
关键词
深度学习
异常检测
无砟轨道道床
无监督检测
deep learning
anomaly detection
ballastless track bed
unsupervised detection
