Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Abstract Condition assessment of municipal sewer pipes using closed circuit television (CCTV) inspections is known to be time consuming, costly, and prone to errors primarily due to operator fatigue or novicity. Automated detection of defects can provide a valuable tool for ensuring the quality, accuracy, and consistency of condition data, while reducing the time and cost of the inspection process. This paper presents an efficient pattern recognition algorithm to support automated detection and classification of pipe defects in images obtained from conventional CCTV inspection videos. The algorithm employs the histograms of oriented gradients (HOG) and support vector machine (SVM) to identify pipe defects. The algorithm involves two main steps: (1) image segmentation to extract suspicious regions of interest (ROI) that represent candidate defect areas; and (2) classification of the ROI using SVM classifier that was trained using sets of HOG features extracted from positive and negative examples of the defect. Proposed algorithm is applied to the problem of detecting tree root intrusions. The performance of linear and radial basis function SVM classifiers evaluated. The algorithm was tested on a set of actual CCTV videos obtained from the cities of Regina and Calgary in Canada. Experimental results demonstrated the viability and robustness of the algorithm.

[1]  Osama Moselhi,et al.  Automated Detection and Classification of Infiltration in Sewer Pipes , 2005 .

[2]  Dulcy M. Abraham,et al.  NEURO-FUZZY APPROACHES FOR SANITARY SEWER PIPELINE CONDITION ASSESSMENT , 2001 .

[3]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[4]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[5]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[6]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[7]  Paul Davis,et al.  Pixel-Based Colour Image Segmentation Using Support Vector Machine for Automatic Pipe Inspection , 2007, Australian Conference on Artificial Intelligence.

[8]  James H. Garrett,et al.  Automated defect detection for sewer pipeline inspection and condition assessment , 2009 .

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[11]  T. Davies,et al.  Sewer pipe deformation assessment by image analysis of video surveys , 1998, Pattern Recognit..

[12]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[13]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Paul Fieguth,et al.  Neuro-fuzzy network for the classification of buried pipe defects , 2006 .

[15]  Paul Fieguth,et al.  Automated detection of cracks in buried concrete pipe images , 2006 .

[16]  Cordelia Schmid,et al.  Shape recognition with edge-based features , 2003, BMVC.

[17]  Stewart Burn,et al.  An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection , 2009 .

[18]  Simon Jörg Sven Kirstein.  Robust adaptive flow line detection in sewer pipes , 2012 .

[19]  Arie Ben-David,et al.  Comparison of classification accuracy using Cohen's Weighted Kappa , 2008, Expert Syst. Appl..

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Anne Ng,et al.  Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models , 2009 .

[22]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[23]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[24]  David G. Stork,et al.  Pattern Classification , 1973 .

[25]  Neil McIntyre,et al.  A database and model to support proactive management of sediment-related sewer blockages. , 2012, Water research.

[26]  Ming-Der Yang,et al.  Systematic image quality assessment for sewer inspection , 2011, Expert Syst. Appl..

[27]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[28]  James H. Garrett,et al.  Visual Pattern Recognition Supporting Defect Reporting and Condition Assessment of Wastewater Collection Systems , 2009 .

[29]  Kaspar Althoefer,et al.  Automated sewer pipe inspection through image processing , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[30]  Mahmoud R. Halfawy,et al.  Integrated Decision Support System for Optimal Renewal Planning of Sewer Networks , 2008 .

[31]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[32]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[33]  Dulcy M. Abraham,et al.  CHALLENGING ISSUES IN MODELING DETERIORATION OF COMBINED SEWERS , 2001 .

[34]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[35]  Pei Liu,et al.  Feature Extraction of Sewer Pipe Defects Using Wavelet Transform and Co-Occurrence Matrix , 2011, Int. J. Wavelets Multiresolution Inf. Process..

[36]  Ming-Der Yang,et al.  Morphological segmentation based on edge detection for sewer pipe defects on CCTV images , 2011, Expert Syst. Appl..

[37]  Ming-Der Yang,et al.  Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis , 2009, Expert Syst. Appl..

[38]  Paul Fieguth,et al.  Segmentation of buried concrete pipe images , 2006 .

[39]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[40]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[41]  Osama Moselhi,et al.  Automated detection of surface defects in water and sewer pipes , 1999 .

[42]  Helge-Björn Kuntze,et al.  Experiences with the development of a robot for smart multisensoric pipe inspection , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[43]  Osama Moselhi,et al.  Classification of Defects in Sewer Pipes Using Neural Networks , 2000 .

[44]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[45]  J. Mashford,et al.  A morphological approach to pipe image interpretation based on segmentation by support vector machine , 2010 .

[46]  Ming-Der Yang,et al.  Automated diagnosis of sewer pipe defects based on machine learning approaches , 2008, Expert Syst. Appl..

[47]  Anna Romanova,et al.  Sewer inspection and comparison of acoustic and CCTV methods , 2013 .

[48]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[49]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .