现代电子技术2024，Vol.47Issue(9)：119-123,5.DOI:10.16652/j.issn.1004-373x.2024.09.022
一种基于归一化流的地铁道床异常检测方法
Metro ballast anomaly detection method based on normalizing flow
摘要
Abstract
It is effective to ensure metro vehicle safe by detecting and locating anomalies of the ballast.Non-supervision-based anomaly detection methods have been widely applied because they only need to be trained by normal images and do not require too much anomaly images which is difficult to collect.Therefore,an unsupervised metro ballast anomaly detection and localization method based on normalizing flow is proposed.Multi-layer feature maps are cross-fused to enhance the model ability to learn image features.A metro ballast dataset is established to train and validate the practicality of the model.It performs better than other same type algorithms.The experimental results on MVTec AD dataset demonstrate that the proposed method increases AUC of 0.109 3 and 0.021 8 in comparison with DifferNet and CS-flow.On ballast dataset,the proposed method obtains recall rate of 95.95%and false alarm rate of 0.908 3%.These results indicate the effectiveness of the model in detecting anomalies in metro ballast and its good generalization ability.This provides a new method for artificial intelligence to replace manual inspection of metro ballast anomalies.关键词
图像处理/异常检测/深度学习/归一化流/计算机视觉/轨道交通Key words
image processing/anomaly detection/deep learning/normalizing flow/computer vision/rail transit分类
信息技术与安全科学引用本文复制引用
甘朗齐,彭朝勇,邱春蓉,罗林..一种基于归一化流的地铁道床异常检测方法[J].现代电子技术,2024,47(9):119-123,5.基金项目
自然基金重点国际合作项目(61960206010) （61960206010）
