Application research of image recognition technology based on improved SVM in abnormal monitoring of rail fasteners

被引:5
作者
Fan, Xianzheng [1 ]
Jiao, Xiongfeng [1 ]
Shuai, Mingming [1 ]
Qin, Yi [1 ]
Chen, Jun [1 ]
机构
[1] China Railway Shanghai Design Inst Grp Corp Ltd, Engn Invest & Survey Design Inst, Shanghai, Peoples R China
关键词
SVM; rail fasteners; image recognition; HOG; LBP feature; PREDICTION; SYSTEM; FEATURES; MODEL;
D O I
10.3233/JCM-226723
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Railway transportation is the main means of transportation for people and the main way of logistics transportation, playing an important role in daily life. Therefore, the safety inspection of railway track has been widely valued. The abnormal intelligent detection of rail fasteners is the key content of rail safety detection. The traditional rail fastener detection method is based on machine learning for image recognition, such as SVM, to detect abnormal rail fasteners. But the traditional method has two defects. The first point is that the detection time is long, and the second point is that the detection accuracy is low. To solve this problem, a rail fastener anomaly detection model based on SVM optimized by IFOA algorithm is proposed. Firstly, the image of rail fastener is collected and filtered; Then, edge detection and image segmentation are performed to obtain the image of the target area; Finally, the HOG feature and LBP feature of the image are extracted, and the improved IFOA-SVM is used to recognize and classify the features, so as to achieve intelligent rail fastener anomaly detection. The experimental results show that when the IACO-SVM model is iterated to 254 times, the fitness value tends to be stable, which is 0.24. The detection accuracy of the model reaches 99.82%, which is higher than the traditional models, and can meet the work requirements of rail fastener anomaly detection. The rail fastener anomaly detection model based on SVM can improve the efficiency of rail fastener anomaly detection, and has a positive effect on the normal operation of railway transportation. However, the number of experimental samples used in the study is limited, which may lead to some errors in the experimental results. Therefore, it is necessary to increase the number of samples in subsequent studies.
引用
收藏
页码:1307 / 1319
页数:13
相关论文
共 21 条
[1]   Photovoltaic cell defect classification using convolutional neural network and support vector machine [J].
Ahmad, Ashfaq ;
Jin, Yi ;
Zhu, Changan ;
Javed, Iqra ;
Maqsood, Asim ;
Akram, Muhammad Waqar .
IET RENEWABLE POWER GENERATION, 2020, 14 (14) :2693-2702
[2]   Colon cancer prediction on histological images using deep learning features and Bayesian optimized SVM [J].
Babu, Tina ;
Singh, Tripty ;
Gupta, Deepa ;
Hameed, Shahin .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (05) :5275-5286
[3]  
Buckley J, 2019, PERMANENT WAY I J RE, V137, P21
[4]   Hybrid fuzzy logic with SVM based prediction analysis model to predict innovation performance of 3C Industry [J].
Chang, Chiu-Lan ;
Fang, Ming .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) :8485-8492
[5]   Real-Time Inspection System for Ballast Railway Fasteners Based on Point Cloud Deep Learning [J].
Cui, Hao ;
Li, Jian ;
Hu, Qingwu ;
Mao, Qingzhou .
IEEE ACCESS, 2020, 8 :61604-61614
[6]   Intelligent English teaching prediction system based on SVM and heterogeneous multimodal target recognition [J].
Dong, Sheng .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 38 (06) :7145-7154
[7]   Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels [J].
Gola, Jessica ;
Webel, Johannes ;
Britz, Dominik ;
Guitar, Agustina ;
Staudt, Thorsten ;
Winter, Marc ;
Mucklich, Frank .
COMPUTATIONAL MATERIALS SCIENCE, 2019, 160 :186-196
[8]   Automatic Rail Surface Defects Inspection Based on Mask R-CNN [J].
Guo, Feng ;
Qian, Yu ;
Rizos, Dimitris ;
Suo, Zhi ;
Chen, Xiaobin .
TRANSPORTATION RESEARCH RECORD, 2021, 2675 (11) :655-668
[9]  
Kuppan Chetty R.M., 2020, J MECH, V15, P168, DOI [10.26782/jmcms.2020.03.00014, DOI 10.26782/JMCMS.2020.03.00014]
[10]   Training prediction and athlete heart rate measurement based on multi-channel PPG signal and SVM algorithm [J].
Lei, Tang ;
Cai, Zhu ;
Hua, Luo .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) :7497-7508