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HASSIP/DFG-SPP1114 Workshop

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Detection of Cardboard Faults during the Production Process Nata a Baba ev, Marko Barjaktarovi University of Belgrade, Faculty of Electrical Engineering – PowerPoint PPT presentation

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Title: HASSIP/DFG-SPP1114 Workshop


1
Detection of Cardboard Faults during the
Production Process
  • Nataša Babacev, Marko Barjaktarovic
  • University of Belgrade, Faculty of Electrical
    Engineering
  • Desanka Radunovic
  • University of Belgrade, Faculty of Mathematics
  • Belgrade, Serbia and Montenegro
  • Bremen, Germany, 23.01.-26.01.2006.

2
Introduction
  • Production of cardboard in a long bolt
  • Occurrence of faults and stains on the surface
  • Detection and localization in real-time
  • Existing algorithm using Kirsch operator
  • Discrete Wavelet Transform using
  • third level Haar wavelets
  • Denoising and selection of
  • vertical wavelet coefficients

3
Cardboard Production
  • Cardboard exits the production machine
  • Optoelectronic system photographs the surface
  • 8001024 pixels with 256 levels of gray
  • Influence of the factory lights and optic lance
  • Nonuniform distribution of gray
  • Preprocessing of the image in order to get almost
    uniform distribution

4
Original image of cardboard
5
Almost uniform distribution
6
Image with fault and noise
7
Existing algorithm
  • Wavelets represent the optimization of an
    existing algorithm by Marko Barjaktarovic
  • High level of noise
  • due to short time of exposition and
  • high speed of the cardboard
  • Denoising is done by a filter of size 1x5 pixels
  • Extracting of the edges of faults is done by
    using the modification of Sobel operator,
  • i.e. Kirsch operator 5x5 matrix

8
Result of the Kirsch operator
9
Converting to binary image
  • The threshold is computed from the histogram

10
Denoising using erosion
  • Clearing the image of dots that
  • Kirsch operator detects as an edge
  • because of local variations of gray
  • The value of every pixel is replaced with the
    minimum value of neighboring pixels with the
    same -coordinate and or in
    order to preserve the line faults that occur
  • Two consecutive erosions are needed

11
Erosion of the line fault
12
Line faults
  • For a fault in a shape of thin line the result is
    more dots with the same -coordinate
  • Distribution of area of all faults objects
    on the image on -coordinate
  • If the area is large for a narrow value of
  • then it is considered a line fault

13
Distribution of area of a line fault
14
Optimization of the algorithm
  • Denoising is done prior the edge detection
  • Edge detection
  • Both are done using third level Haar wavelets

15
Denoising with DWT level 3 Haar
  • Soft thresholding
  • Fixed form threshold t
  • s median absolute deviation of detail
    coefficients of scale 1

16
Denoised image with level 3 Haar
17
Extracting the edges
  • DWT using third level Haar wavelets
  • Faults occur in the direction of motion, i.e.
    vertical direction
  • Only vertical detail coefficients are kept

18
IDWT with vertical coefficients
19
Binary values of pixels
20
Comparing the two methods
  • Denoised image with DWT is clearer then
    in the existing algorithm
  • Faster then detecting edges by Kirsch operator
  • Less data stored in a sparse matrix
  • After IDWT image is with less noise
  • Erosion is still needed after IDWT

21
Image without faults after IDWT
22
Most common example of fault
23
Denoising with DWT level 3 Haar
24
Binary image of IDWT
25
Image after two erosions of IDWT
26
Further optimization
  • The algorithm using wavelets for edge detection
    is still in development
  • Possible optimization
  • leaving less vertical coefficients as non-zero
  • The right threshold needs to be determined

27
Detection of Cardboard Faults during the
Production Process
  • Nataša Babacev, Marko Barjaktarovic
  • University of Belgrade, Faculty of Electrical
    Engineering
  • Desanka Radunovic
  • University of Belgrade, Faculty of Mathematics
  • Belgrade, Serbia and Montenegro
  • Bremen, Germany, 23.01.-26.01.2006.
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