Insulator Fault Detection using Image Processing Master Thesis Submitted in Fulfilment of the Requirements for the Academic Degree M.Sc. Dept. of Computer Science Chair of Computer Engineering Submitted by: Abhik Banerjee Student ID: 434058 Date: 13.08.2018 Supervising tutor: Prof. Dr. W. Hardt Prof. Dr. Uranchimeg Tudevdagva M.Sc. Batbayar Battseren Abstract This thesis aims to present a method for detection of faults (burn marks) on insulator using only image processing algorithms. It is accomplished by extracting the insulator from the background image and then detecting the burn marks on the segmented image. Apart from several other challenges encountered during the detection phase, the main challenge was to eliminate the connector marks which might be detected as burn-marks. The technique discussed in this thesis work is one of a kind and not much research has been done in areas of burn mark detection on the insulator surface. Several algorithms have been pondered upon before coming up with a set of algorithms applied in a particular manner. The first phase of the work emphasizes on detection of the insulator from the image. Apart from pre-processing and other segmentation techniques, Symmetry detection and adaptive GrabCut are the main algorithms used for this purpose. Efficient and powerful algorithms such as feature detection and matching were considered before arriving at this method, based on pros and cons. The second phase is the detection of burn marks on the extracted image while eliminating the connector marks. Algorithms such as Blob detection and Contour detection, adapted in a particular manner, have been used for this purpose based on references from medical image processing. The elimination of connector marks is obtained by applying a set of mathematical calculations. The entire project is implemented in Visual Studio using OpenCV libraries. Result obtained is cross-validated across an image data set. Keywords: Insulator detection, Burn-mark detection, GrabCut, Symmetry detection, Blob detection, Image processing 2 Content Abstract ....................................................................................................................... 2 Content ........................................................................................................................ 3 List of Figures .............................................................................................................. 6 List of Tables ............................................................................................................... 9 List of Abbreviations .................................................................................................. 10 1 Introduction ........................................................................................................ 11 1.1 Motivation ..................................................................................................... 12 1.2 Research objectives of the thesis ................................................................. 14 1.3 Problem Statement....................................................................................... 14 1.4 Overview of the following chapters ............................................................... 15 2 Fundamentals of Image Processing ................................................................... 17 2.1 Image Understanding basics ........................................................................ 17 2.1.1 Properties of an Image .......................................................................... 17 2.1.2 Histogram Equalization .......................................................................... 19 2.1.3 Blurring or Smoothening of Image using filters ...................................... 20 2.1.4 Thresholding .......................................................................................... 21 2.1.5 Morphological Transformation ............................................................... 22 2.2 Feature detection and matching ................................................................... 25 2.2.1 Feature detection ................................................................................... 25 2.2.2 Feature description ................................................................................ 26 2.2.3 Feature matching ................................................................................... 27 2.2.4 Feature alignment .................................................................................. 28 2.3 Image processing tools ................................................................................ 28 2.3.1 OpenCV ................................................................................................. 28 2.3.2 MATLAB ................................................................................................ 29 2.3.3 Scikit-image ........................................................................................... 30 2.3.4 LEADTOOLS ......................................................................................... 30 3 State of Art ......................................................................................................... 32 3 3.1 Object detection using Template Matching .................................................. 32 3.2 Object detection using Feature detection and matching............................... 34 3.3 Insulator detection techniques ...................................................................... 38 3.3.1 Detection of snow on Insulator caps. ..................................................... 38 3.3.2 Recognition of tempered glass insulator ................................................ 40 3.3.3 Insulator detection using regular texture pattern .................................... 41 3.3.4 Insulator detection using texture feature sequence ............................... 42 3.3.5 Insulator fault detection using gradient based descriptor. ...................... 43 3.4 Algorithms for foreground extraction ............................................................ 45 3.4.1 Magic Wand ........................................................................................... 46 3.4.2 Intelligent Scissors ................................................................................. 46 3.4.3 Bayes Matting ........................................................................................ 47 3.4.4 Level Sets .............................................................................................. 47 3.4.5 Graph Cut .............................................................................................. 48 3.4.6 GrabCut ................................................................................................. 48 3.5 Methods to detect Burn Marks ..................................................................... 50 3.5.1 Contour detection .................................................................................. 51 3.5.2 Blob detection ........................................................................................ 52 4 Concept .............................................................................................................. 55 4.1 Insulator detection ........................................................................................ 58 4.1.1 Symmetry detection ............................................................................... 59 4.1.2 Foreground extraction ............................................................................ 63 4.2 Defining ROI ................................................................................................. 64 4.2.1 Contour detection .................................................................................. 64 4.2.2 Measurement of extreme points ............................................................ 65 4.2.3 Removal of connector marks based on pixel measurement .................. 66 4.3 Burn marks detection ................................................................................... 67 5 Implementation ................................................................................................... 71 5.1 Insulator detection ........................................................................................ 72 5.1.1 Algorithm for Symmetry detection .......................................................... 72 4 5.1.2 Algorithm for foreground extraction using GrabCut ................................ 73 5.2 Defining ROI ................................................................................................. 76 5.2.1 Contour detection algorithm ................................................................... 76 5.2.2 Extreme point measurement and removal of connector marks .............. 82 5.3 Burn marks detection ................................................................................... 93 6 Result ................................................................................................................. 98 7 Conclusion and Future Scope .......................................................................... 104 7.1.1 Summary of the thesis ......................................................................... 104 7.1.2 Conclusion ........................................................................................... 105 7.1.3 Future Scope ....................................................................................... 106 Bibliography ............................................................................................................