Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM

被引:22
作者
Jin, Cheng [1 ,2 ]
Kong, Xianguang [1 ]
Chang, Jiantao [1 ]
Cheng, Han [1 ]
Liu, Xiaojia [2 ]
机构
[1] Xidian Univ, Sch Mechanoelect Engn, Xian 710071, Peoples R China
[2] Shanghai Spaceflight Precis Machinery Inst, Res Ctr, Shanghai 201600, Peoples R China
基金
中国国家自然科学基金;
关键词
Internal crack detection; Feature extraction; Relief algorithm; Adaboost-SVM; FUSION;
D O I
10.1007/s00170-020-05368-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The internal crack defect is prone to appear in the inner part of casting products during the production process due to the influence of casting craft and on-site environment. In order to ensure the internal quality of the casting products, nondestructive examination and defect detection technology should be used to detect the internal crack of the casting product. The existing defect detection technologies have some problems such as poor generalization and low accuracy. Therefore, an internal crack defect detection method based on the Relief algorithm and Adaboost-SVM is proposed in this paper. Firstly, casting image is preprocessed by grayscale transformation, bilateral filtering, and adaptive image segmentation. Secondly, HOG feature, invariant moment feature, and LBP feature are extracted, and sensitive feature set is selected by Relief algorithm. Finally, the Adaboost-SVM is used to construct the internal crack detection model to realize the crack detection with high generalization and accuracy. The effectiveness of the method is verified by the casting image dataset collected in the actual industrial field. The experimental result reveals that the proposed method could not only extract sensitive feature set but also has better classification performance and generalization ability than other common classifiers.
引用
收藏
页码:3313 / 3322
页数:10
相关论文
共 31 条
[1]  
[Anonymous], 2012, WORLD ACAD SCI ENG T
[2]  
[Anonymous], 2006, Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, DOI [DOI 10.1109/CVPR.2006.119, 10.1109/CVPR.2006.119]
[3]  
Beyerer J, 2015, AUTOMATED VISUAL INS, P9530
[4]   Multilevel image segmentation with adaptive image context based thresholding [J].
Bhattacharyya, Siddhartha ;
Maulik, Ujjwal ;
Dutta, Paramartha .
APPLIED SOFT COMPUTING, 2011, 11 (01) :946-962
[5]   Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform [J].
Bhutada, G. G. ;
Anand, R. S. ;
Saxena, S. C. .
DIGITAL SIGNAL PROCESSING, 2011, 21 (01) :118-130
[6]   Automated detection of welding defects in pipelines from radiographic images DWDI [J].
Boaretto, Neury ;
Centeno, Tania Mezzadri .
NDT & E INTERNATIONAL, 2017, 86 :7-13
[7]  
Da Silva R.R., 2002, Journal of Nondestructive Testing, V7, P1
[8]   Histograms of oriented gradients for human detection [J].
Dalal, N ;
Triggs, B .
2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, :886-893
[10]  
Ding Shumin, 2011, 2011 International Conference on Multimedia Technology, P2903