Title: HASSIP/DFG-SPP1114 Workshop
1Detection 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.
2Introduction
- 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
3Cardboard 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
4Original image of cardboard
5Almost uniform distribution
6Image with fault and noise
7Existing 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
8Result of the Kirsch operator
9Converting to binary image
- The threshold is computed from the histogram
10Denoising 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
11Erosion of the line fault
12Line 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
13Distribution of area of a line fault
14Optimization of the algorithm
- Denoising is done prior the edge detection
- Edge detection
- Both are done using third level Haar wavelets
15Denoising with DWT level 3 Haar
- Soft thresholding
- Fixed form threshold t
- s median absolute deviation of detail
coefficients of scale 1
16Denoised image with level 3 Haar
17Extracting 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
18IDWT with vertical coefficients
19Binary values of pixels
20Comparing 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
21Image without faults after IDWT
22Most common example of fault
23Denoising with DWT level 3 Haar
24Binary image of IDWT
25Image after two erosions of IDWT
26Further 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
27Detection 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.