快速特征金字塔和Soft-Cascade在折角塞门图像故障检测中的应用

孙国栋; 林凯; 高媛; 张杨; 赵大兴

doi:10.13433/j.cnki.1003-8728.20190078

快速特征金字塔和Soft-Cascade在折角塞门图像故障检测中的应用

doi: 10.13433/j.cnki.1003-8728.20190078

1.
湖北工业大学机械工程学院, 武汉 430068
2.
南京大学计算机科学与技术系, 南京 210023

基金项目:

江苏省自然科学基金项目 BK20150016

国家自然科学基金项目 51775177

国家自然科学基金项目 51675166

详细信息

作者简介:
孙国栋(1981-), 教授, 硕士生导师, 研究方向为机器视觉、机器学习, sgdeagle@163.com

中图分类号: TN911.73
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- 文章访问数: 315
- HTML全文浏览量: 122
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2018-12-26
- 刊出日期: 2019-06-05

Application of Fast Feature Pyramids and Soft-Cascade in Image Fault Detection for Angle Cock

1.
School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China
2.
Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China

摘要

摘要: 为了提升列车折角塞门的故障检测效率，提出了一种基于快速特征金字塔和Soft-Cascade的故障图像检测算法。首先，构建快速特征金字塔模型来提取图像多尺度聚合通道特征；其次，利用向量化后的多尺度聚合通道特征来训练Soft-Cascade故障分类器；最后，利用训练好的分类器来判断待检折角塞门是否含有故障。实验结果表明：该算法的故障检测正确率为97.33%，离线检测速度高达43 fps（每张图像仅需23 ms），检测效率高于其他算法。该算法训练时间短，检测速度快，硬件要求低，能满足列车折角塞门的故障检测要求。
- 机器视觉 /
- 折角塞门 /
- 快速特征金字塔 /
- Soft-Cascade算法
Abstract: The fault detection algorithm based on the fast feature pyramids and Soft-Cascade was proposed in order to improve the efficiency of the fault detection for the angled cock. Firstly, the fast feature pyramids model was constructed to extract the image multi-scale aggregate channel features. Secondly, the vectorized multi-scale aggregate channel features was used to train the Soft-Cascade fault classifier. Finally, the trained classifier was used to detect whether the angle cock contains a fault. The experimental results show that the fault detection accuracy of the proposed algorithm is of 97.33%, the offline detection speed is up to 43 fps (only 23 ms per image), and the detection efficiency is higher than that by using other algorithms. The present algorithm has the short training time, fast detection speed and low hardware requirements, which can meet the requirements of fault detection for the angle cock.
- machine vision /
- angle cock /
- fast feature pyramids /
- Soft-Cascade algorithm

HTML全文

图 1 算法整体框架

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图 2 聚合通道特征的计算流程

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图 3 快速特征金字塔计算流程

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图 4 软级联分类器的结构

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图 5 图像集的正负样本

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图 6 实验中所用的数据库图像

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表 1 实验所用的数据库

名称	正样本	负样本	总数
训练样本	800	800	1600
测试样本	400	400	800

下载: 导出CSV

表 2 不同特征对故障检测的影响

特征组合	CDR/%	MDR/%	FDR/%
Gray	40.03	59.97	0
LBP	63.18	18.17	18.65
GradM	53.7	11.41	34.89
HOG	93.25	4.66	2.09
Gray+LBP	44.21	55.15	0.64
GradM+LBP	46.95	53.05	0
Gray+GradM	57.55	0.81	41.64
Gray+HOG	92.45	4.82	2.73
GradM+HOG	94.25	4.50	1.25
LBP+HOG	90.04	2.25	7.71
Gray+GradM+HOG	92.77	3.05	4.18
GradM+HOG+LBP	97.33	1.28	1.39

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表 3 不同置信度阈值对故障检测的影响

置信度阈值	CDR/%	MDR/%	FDR/%
0	89.24	5.78	4.98
5	89.22	5.47	5.31
10	89.87	4.50	5.63
15	90.03	3.54	6.43
20	95	2.41	2.59
25	97.33	1.28	1.39
30	94.88	0.80	4.32
35	89.87	0.48	9.65
40	88.75	0.48	10.77
45	87.79	0.16	12.05
50	87.79	0	12.21

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表 4 不同算法性能对比结果

算法	CDR/%	MDR/%	FDR/%	训练时间/s	测试时间/s
LBP+Cascade	48.87	51.13	0	76	0.048
HOG+Cascade	82.96	16.56	0.48	151	0.115
本文算法	97.33	1.28	1.39	84	0.023

下载: 导出CSV

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