Panoramic Crack Detection for Steel Beam Based on Structured Random Forests

Condition monitoring and fault diagnosis are the most important process in manufacturing industries. In this paper, a steel beam panoramic crack detection method based on structured random forests has been proposed to obtain more efficient maintenance of manufacturing equipment. The structured random forests method and semi-reconstruction method of anti-symmetrical bi-orthogonal wavelets are combined to detect the edges of the cracks. Candidate features of the crack images are randomly chosen to train the crack classifier. Besides, the fast-multi-image stitching method is applied to stitch the entire image. The generated crack detection classifier is also used to determine the classification by voting the feature vector of each image. The prescribed characteristics, i.e., area, height, and weight, are introduced to select those cracks that satisfy the prescribed conditions. The experimental results show that the approach is effective and efficient in recognizing the surface cracks of the panoramic steel beam.

[1]  Shen Liqun,et al.  Automatic inspection of weld defects using X-ray image sequences , 2007 .

[2]  Michael S. Brown,et al.  As-Projective-As-Possible Image Stitching with Moving DLT , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  S. M. Elaraby,et al.  Welding defect detection from radiography images with a cepstral approach , 2011 .

[4]  Aysin Ertüzün,et al.  An efficient method for texture defect detection: sub-band domain co-occurrence matrices , 2000, Image Vis. Comput..

[5]  Anirban Mukherjee,et al.  Automatic Defect Detection on Hot-Rolled Flat Steel Products , 2013, IEEE Transactions on Instrumentation and Measurement.

[6]  Mohammed Bennamoun,et al.  Optimal Gabor filters for textile flaw detection , 2002, Pattern Recognit..

[7]  Ke Xu,et al.  Application of Hidden Markov Tree Model to On-line Detection of Surface Defects for Steel Strips , 2013 .

[8]  Tony Lindeberg,et al.  An automatic assessment scheme for steel quality inspection , 2000, Machine Vision and Applications.

[9]  Dong De-wei.  Convex Active Contour Segmentation Model of Strip Steel Defects Image Based on Local Information , 2012 .

[10]  Liqiang Nie,et al.  Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm , 2016, Neurocomputing.

[11]  Yunlin Luo,et al.  Fault diagnose of aero engine based on digital image processing , 2008, 2008 Chinese Control and Decision Conference.

[12]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  T. Warren Liao,et al.  An automated radiographic NDT system for weld inspection: Part I — Weld extraction , 1996 .

[14]  Yueming Li,et al.  An automated radiographic NDT system for weld inspection: Part II—Flaw detection , 1998 .

[15]  Gang Li,et al.  Image-based Method for Concrete Bridge Crack Detection ? , 2013 .

[16]  Allen B. Downey,et al.  Primitive-Based Classification of Pavement Cracking Images , 1993 .

[17]  B. Suvdaa,et al.  Steel Surface Defects Detection and Classification Using SIFT and Voting Strategy , 2012 .

[18]  Yao La.  Pantograph Slide Cracks Detection Method Based on Fuzzy Entropy and Hough Transform , 2014 .

[19]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[20]  Zhang Xiao-guang.  Weld Defects Distinguishing Method Based on Fuzzy Neural Networks , 2003 .

[21]  Hu Hui-ju.  Steel strip surface defects classification based on machine learning , 2014 .

[22]  J. López-Higuera,et al.  Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks , 2007 .

[23]  Anders Landström,et al.  Morphology-Based Crack Detection for Steel Slabs , 2012, IEEE Journal of Selected Topics in Signal Processing.

[24]  R. Haralick.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  A. Kehoe,et al.  Image processing for industrial radiographic inspection : image enhancement , 1990 .

[27]  Tian Yan-ping.  Real-time automatic detection of weld defects in X-ray images , 2004 .

[28]  Yan Yunhui.  Reseach on recognition and classification of strip steel surface defect image——Based on method of mixed weighting features and RBF network , 2007 .

[29]  Li Cheng.  Recognition of Weld Defect Types , 2010 .

[30]  S. Divya,et al.  Mathematical morphology and bottom-hat filtering approach for crack detection on relay surfaces , 2013, INTERNATIONAL CONFERENCE ON SMART STRUCTURES AND SYSTEMS - ICSSS'13.

[31]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[32]  Shuichi Fukuda,et al.  Development of an Expert System for Welding Design Support : an Attempt(Welding Mechanics, Strength & Design) , 1985 .

[33]  Yigang He,et al.  A cost-effective and automatic surface defect inspection system for hot-rolled flat steel , 2016 .

[34]  Tu Hong-bin.  A Modified Algorithm of Image Segmentation for Bearing Surface DefectsBased on K-means , 2007 .

[35]  Nicola Ancona,et al.  Rail corrugation detection by Gabor filtering , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[36]  C. Lawrence Zitnick,et al.  Fast Edge Detection Using Structured Forests , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Norbert Meyendorf,et al.  USE OF AUTOMATIC IMAGE PROCESSING FOR MONITORING OF WELDING PROCESSES AND WELD INSPECTION , 1989 .

[38]  Jaime A. Camelio,et al.  Real-time fault detection in manufacturing environments using face recognition techniques , 2010, Journal of Intelligent Manufacturing.

[39]  Han Ying-li.  Classification and Recognition Based on Improved SVM for Surface Defects of Cold Strips , 2007 .

[40]  A. O. Martins Luiz,et al.  Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[41]  Zhao Li-hong.  Defect Extraction in the Welding-Line X-Ray Inspection Image , 2007 .