PCB Inspection Using Image Processing and Wavelet
Transform
Joaquín Santoyo1, J. Carlos Pedraza2, L. Felipe Mejía1, and Alejandro Santoyo3
1
Universidad Tecnológica de Querétaro, Av. Pie de la Cuesta s/n Lomas de San Pedrito
Peñuelas Querétaro, Qro. C.P. 76148 México
jsantoyo@uteq.edu.mx
2
Centro de Ingeniería y Desarrollo Industrial, Av. Pie de la Cuesta No. 702 Desarrollo san
Pablo, Querétaro, Qro. C.P. 76130 México
jpedraza@cidesi.mx
3
Universidad Autónoma de Querétaro, Centro Universitario, Cerro de las Campanas
Querétaro, Qro. C.P. 76010 México
alex@uaq.mx
Abstract. In electronics mass-production manufacturing, printed circuit board
(PCB) inspection is a time consuming task. Manual inspection does not
guarantee that PCB defects can be detected. In this paper, a spatial filtering and
wavelet-based automatic optical inspection system for detect PCB defects is
presented. This approach combines wavelet image compression utility and
spatial filtering. Defects are detected by subtracting the approximations of
reference image wavelet transform and test image wavelet transform followed
by a median filter stage. Finally, defect image is obtained by computing the
inverse wavelet transform. Advantages of this approach are also described.
1 Introduction
Manual inspection of printed circuit boards (PCB’s) has been displaced by automatic
optical inspection due to human limitations. Automatic optical inspection (AOI) do
the same labor as manual inspection with great advantages: it can recognize line
width errors, shorts, pinholes, etc. Automatic inspection also has advantages over
electrical contact testing: it can detect possible electric leakages, inductance and
capacitance parasite due to nicks, mousebite etc.
Inspection methods can be grouped in three main categories, as stated by Moganti
et al. [1]: referential methods, rule-based methods and hybrid methods. Referential
method needs a “good” sample to compare point-to-point with a regular sample. It has
the disadvantage that some process deviations could be interpreted as defects.
Non-referential methods or sometimes called design-rule verification method, do
not require a gold sample to work with; they work with the PCB specifications design
as a set of rules.
Finally hybrid methods combine the advantages of referential and rule-base
methods. Unfortunately this approach is a time consuming method due to double
check.
A. Gelbukh and A.F. Kuri Morales (Eds.): MICAI 2007, LNAI 4827, pp. 634–639, 2007.
© Springer-Verlag Berlin Heidelberg 2007
PCB Inspection Using Image Processing and Wavelet Transform
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Wavelet transform has been used in vision field for different applications:
fingerprint recognition [2], medical image analysis [3], image noise removal [4],
image compression [5], and others.
Although some work on printed circuit board verification have been developed in
recent years [6] , there are some aspects like image denoising that can be explored
using other techniques.
The aim of this work is find defects in printed circuit boards combining the recent
wavelet transform for improve data processing and spatial filtering to reduce or
eliminate noise in a proper way.
2 Wavelets
The continuous Wavelet Transform f (a, b) of a continuous signal x(t ) is defined [7]:
f (a, b) =
∞
∫ x(t )
−∞
1 ⎛t −b⎞
Ψ⎜
⎟dt .
a ⎝ a ⎠
(1)
To assure the perfect reconstruction of the original signal, the wavelet Transform.
must satisfy
CΨ =
∞
∫
−∞
Ψ ' (ω )
ω
2
dω < ∞
(2)
where Ψ (ω ) denotes wavelet Fourier transform. This condition is the so-called
admissibility criterion for the wavelet ψ (t ) .
The continuous wavelet transform is difficult to evaluate for all scales and
positions. Using the Discrete Wavelet Transform a suitable set of scales and positions
can be chosen.
The discrete wavelet transform of an image or function f ( x, y ) of size M x N is
'
Wϕ ( j0 , m, n) =
1
MN
W i ( j , m, n) =
1
MN
M −1 N −1
∑ ∑ f ( x, y )ϕ
x =0
y =0
M −1 N −1
∑ ∑ f ( x, y)ψ
x =0
i
j0 , m ,n
( x, y )
(3)
j ,m ,n
( x, y )
(4)
y =0
Where i ={ H, V, D}
3 Algorithm Proposal
This proposal is a model-based method due to the comparison of a reference image
and a test image. A second level Haar wavelet is applied to reference image. Haar
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wavelet was chosen due to this wavelet is simplest, fastest and suitable for this
approach [8]. The wavelet output approximations (coef1) are stored in memory and
this step is made just once. A second level Haar wavelet is applied to test image and
then the output wavelet transform (coef2) is subtracted from memory. The resulting
matrix (coef3) has the information of defects if any and some noise. Noise is
eliminated thresholding absolute values of coef3 and then applying a 5 x 5 median
filter. This algorithm can be resumed as follows (Fig 1):
ƒ
ƒ
ƒ
ƒ
ƒ
coef1(x,y) Second level approximation Haar wavelet transform of reference
image.
coef2(x,y) Second level approximation Haar wavelet transform of test image.
coef3(x,y) = coef1(x,y ) - coef1(x,y)
if coef3(x,y) > t then coef3(x,y)=1, if not, coef3(x,y)=0.
Coef4(x,y) = Defecto(m,n) = inverse wavelet transform of coef4(x,y)
coef1(x,y)
Reference
Image
Im1(x,y)
Coef3(x,y)>t
Wavelet
Coef3(x,y)
coef1(x,y)-coef2(x,y)
Transform
coef3(x,y) h(x,y)
coef2(x,y)
Test
Image
Im2(x,y)
Coef4(x,y)
Inverse
Wavelet
Transform
Wavelet
defecto(x,y)
PCB defects
Transform
Fig. 1. Defect detection algorithm
4 Experiments and Results
Two images of size 1000 x 1000 were used for testing this approach. Reference and
test images are gray scale images of a real printed circuit board. Test image is a
defective PCB corrupted with salt & pepper noise (Fig 2). The alignment of the test
image is made manually. The approximation part of second level Haar wavelet for the
reference image and test image is calculated. The size of the resulting approximation
matrix is 250 x 250. Only the approximation part of the second level wavelet
transform is used due to the great advantage of having all the meaningful information
of the original image in just ¼ of the original size.
PCB Inspection Using Image Processing and Wavelet Transform
637
Fig. 2. a) Reference Image b) Test image
Fig. 3. Approximations for a) reference image b) test image
For this implementation was found that 6 was the best threshold value before
filtering. Several window filter sizes were used to eliminate the image noise; a 5 x 5
window size gave the best results. Finally second level Haar wavelet inverse
transform is applied to coef3(x,y) and it is obtained an image that contains the
detected defects (fig 4).
Fig. 4. a) Difference image with noise b) Defects detected after filtering
A classical image processing approach to detect defects was implemented at same
time to compare results with the proposed algorithm. The stages for this approach
were binarization, image difference and filtering all in full resolution. Defects were
found without problem. Table 1 shows the comparison of operation time for every
step for both implementations. Using the Wavelet-based approach a time reduction
about 50% is obtained. The time consuming task is filtering. In both approaches,
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filtering is used but in different domain: Wavelet–based (1/4 of original size) and full
resolution. Due to this data compression, a reduction time in data processing can be
obtained.
Table 1. Inspection time for a) classical image processing approach b) wavelet.based approach
Operation time (s)
Operation
Read image
Second wavelet transform
Binarization
Image difference
Median filtering
Inverse wavelet
Total
Classical approach
0.546
Not used
0.078
0.047
0.813
Not used
1.484
Wavelet-based
0.546
0.094
Not used
0.001
0.094
0.026
0.761
Matlab was chosen as a platform to develop and test the algorithm using the
Matlab’s Wavelets Toolbox. Tests were implemented in a laptop with a Centrino Duo
processor.
5 Conclusions and Future Work
The wavelet transform approximation is a compressed image that preserves the
meaningful information necessary to complete the image processing and be able to
detect any defect.
The combination of using wavelets transforms and spatial filtering used in this
approach has shown that defects in a printed circuit board can be detected in a similar
way as a classical image processing method can do. This represents a great advantage
when the verification of PCB’s is in real time due to reduction of processing data.
Future work will be directed towards the classification of the defects not just found
them. Aspects relative to defects like size, position, orientation and classification
techniques must be investigated
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