基于结构相似深度卷积自编码的异常扣件检测模型

李清勇; 王建柱; 祝叶舟; 黄祺隆; 彭文娟; 王胜春; 戴鹏

doi:10.19818/j.cnki.1671-1637.2022.04.014

基于结构相似深度卷积自编码的异常扣件检测模型

doi: 10.19818/j.cnki.1671-1637.2022.04.014

1.
北京交通大学交通数据分析与挖掘北京市重点实验室，北京 100044
2.
北京交通大学智慧高铁系统前沿科学中心，北京 100044
3.
中国铁道科学研究院集团有限公司基础设施检测研究所，北京 100081

基金项目:

国家自然科学基金项目 U2034211

中央高校基本科研业务费专项资金项目 2022JBQY007

详细信息

作者简介:
李清勇(1979-)，男，湖南涟源人，北京交通大学教授，工学博士，从事人工智能研究

中图分类号: U213.5
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- 文章访问数: 383
- HTML全文浏览量: 152
- PDF下载量: 71
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-02
- 网络出版日期: 2022-10-08
- 刊出日期: 2022-08-25

Abnormal fastener detection model based on deep convolutional autoencoder with structural similarity

1.
Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
2.
Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China
3.
Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing 100081, China

Funds:

National Natural Science Foundation of China U2034211

Fundamental Research Funds for the Central Universities 2022JBQY007

More Information

Author Bio:
LI Qing-yong(1979-), male, professor, PhD, liqy@bjtu.edu.cn

摘要

摘要: 介绍了轨道扣件系统的基本功能，概述了已有的轨道异常扣件检测技术，归纳了基于机器视觉的传统检测方法和深度学习方法所关注的问题及存在的不足; 介绍了自编码的基本思想与形式化过程，提出了一种基于编解码架构的异常扣件检测模型; 分析了传统像素级图像相似度评价指标的缺陷，实现了基于结构相似的损失函数和图像异常判定; 构建了轨道扣件图像数据集，验证了模型的性能; 将代表性的误报与漏报图像可视化，描述了这些图像的表观特征，分析了发生漏报与误报可能的原因。研究结果表明：结构化相似指标显著提升了模型的检测性能，与具有相同网络架构但使用平均绝对误差和均方误差作为相似度评价指标的检测模型相比，模型的F值分别提升了14.5%和16.2%;与其他对比模型相比，提出的模型取得了最高的检测精确率和F值，分别达到了98.6%和98.1%，与次优的RotNet模型相比分别提升了6.0%和9.8%;召回率为97.1%，略低于深度支持向量数据描述(DSVDD)模型的98.4%;整体上看，F值比所有对比模型均高出超过9%，提出的模型表现出了明显的性能优势。
- 轨道扣件 /
- 异常扣件检测 /
- 深度卷积自编码 /
- 无监督学习 /
- 结构化相似
Abstract: The basic functions of rail fastener system were presented, the existing detection technologies for abnormal rail fasteners were outlined, and the concerns and shortcomings of machine vision-based traditional methods and deep learning methods were summarized. The basic idea and formalization process of autoencoder were explained, and an abnormal fastener detection model based on an encoding-decoding architecture was proposed. The drawbacks of traditional pixel-level image similarity metrics were analyzed, and the loss function and image abnormality were determined according to the structural similarity. A dataset of rail fastener images was built and utilized to verify the performance of the proposed model. The representative false-positive and false-negative images were visualized, and their appearance features were described, and the possible reasons for the occurrence of false positives and false negatives were analyzed. According to the research results, the detection performance of the proposed model is significantly enhanced by the structural similarity index. The F-value of the proposed model is 14.5% and 16.2%, respectively, higher than those of the models that have the same network architecture but use the mean absolute error and mean square error as the similarity metrics. The highest detection precision and F-value, as high as 98.6% and 98.1%, respectively, are achieved by the proposed model when it is compared with the other models under comparison. They are found to be 6% and 9.8%, respectively, higher than those of the second-best RotNet model. The recall of the proposed model is 97.1%, slightly lower than the 98.4% of the deep support vector data description (DSVDD) model. On the whole, an F-value over 9% higher than those of all the other models under comparison is achieved by the proposed model, representing a significant performance advantage of the proposed model.
- rail fastener /
- abnormal fastener detection /
- deep convolutional autoencoder /
- unsupervised learning /
- structural similarity

HTML全文

图 1 WJ-7型扣件系统

Figure 1. WJ-7 fastener system

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图 2 结构相似深度卷积自编码模型

Figure 2. DCAE model with structural similarity

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图 3 扣件正常样例

Figure 3. Samples of normal fasteners

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图 4 扣件断裂样例

Figure 4. Samples of fractured fasteners

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图 5 扣件移位样例

Figure 5. Samples of dislocated fasteners

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图 6 扣件丢失样例

Figure 6. Samples of missing fasteners

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图 7 典型误报扣件图像

Figure 7. Representative false positive fastener images

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图 8 典型漏报扣件图像

Figure 8. Representative false negative fastener images

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表 1 网络参数设置

Table 1. Parameter setting of network

	卷积核			激活函数	输出尺寸
	尺寸	步长	填充	激活函数	输出尺寸
输入层					3×32×32
编码层1	64×4×4	2	1	f₁(·)	64×16×16
编码层2	128×4×4	2	1	f₂(·)	128×8×8
编码层3	256×4×4	2	1	f₃(·)	256×4×4
编码层4	512×4×4	2	1	f₄(·)	512×2×2
编码层5	64×4×4	2	1		64×1×1
解码层1	512×4×4	2	1	g₁(·)	512×2×2
解码层2	256×4×4	2	1	g₂(·)	256×4×4
解码层3	128×4×4	2	1	g₃(·)	128×8×8
解码层4	64×4×4	2	1	g₄(·)	64×16×16
解码层5	3×4×4	2	1	g₅(·)	3×32×32

下载: 导出CSV

表 2 KNN模型在不同阈值下的性能

Table 2. Performances of KNN model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	71.7	99.8	83.5
μ+σ	88.5	86.0	87.2
μ+2σ	94.0	49.8	65.1
μ+3σ	96.3	23.7	38.1

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表 3 DSVDD模型在不同阈值下的性能

Table 3. Performances of DSVDD model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	73.9	98.4	84.4
μ+σ	95.7	73.4	83.1
μ+2σ	99.4	43.9	60.9
μ+3σ	99.8	24.3	39.1

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表 4 RotNet模型在不同阈值下的性能

Table 4. Performances of RotNet model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	92.6	84.2	88.3
μ+σ	96.5	68.3	80.0
μ+2σ	97.5	60.9	74.9
μ+3σ	98.0	54.9	70.4

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表 5 DCAE(MAE)模型在不同阈值下的性能

Table 5. Performances of DCAE(MAE) model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	73.5	96.9	83.6
μ+σ	86.7	71.3	78.2
μ+2σ	91.0	40.5	56.1
μ+3σ	93.9	20.6	33.8

下载: 导出CSV

表 6 DCAE(MSE)模型在不同阈值下的性能

Table 6. Performances of DCAE(MSE) model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	75.4	89.7	81.9
μ+σ	86.3	54.7	67.0
μ+2σ	90.0	32.1	47.3
μ+3σ	92.3	18.8	31.3

下载: 导出CSV

表 7 DCAE(SSIM)模型在不同阈值下的性能

Table 7. Performances of DCAE(SSIM) model under different thresholds

阈值	精确率/%	召回率/%	F值/%
μ	75.5	100.0	86.1
μ+σ	91.1	100.0	95.3
μ+2σ	96.4	99.6	98.0
μ+3σ	98.6	97.7	98.1

下载: 导出CSV

表 8 不同模型的检测结果对比

Table 8. Comparison of test results of different models

模型	精确率/%	召回率/%	F值/%
KNN	88.5	86.0	87.2
DSVDD	73.9	98.4	84.4
RotNet	92.6	84.2	88.3
DCAE(MAE)	73.5	96.9	83.6
DCAE(MSE)	75.4	89.7	81.9
DCAE(SSIM)	98.6	97.7	98.1

下载: 导出CSV

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