a vision-based method for fabric defect detection

JOLRNAL OF SCIENCE & TECHNOLOGY * No. 83B-201I

A VISION-BASED METHOD FOR FABRIC DEFECT DETECTION M O T PHUONG PHAP PHAT HIEN LOI VAI DET DUA TREN XU LY ANH

Le Huy Viet, Le Thi Lan, Le Ngoc Thuy Hanoi University of Science and Technology

ABSTRACT

Fabric inspection plays an important role in garment companies. With conventional fabric inspection systems, the human inspectors have to detect and classify defects manually. This work is tedious and expensive. Moreover, defect detection accuracy depends highly in inspector's experience and decreases after a number of working hours. In recent years, several automatic fabric inspection approaches based on computer vision and machine learning techniques have been proposed. In order to employ these approaches in real application, they have to satisfy two main requirements: high accuracy and real-time. In this paper, we present a method for defect detection based on Gabor filter that meets these requirements. The obtained results are very promlsslng.

TOM TAT

Kiim soit chit Iwgng vii ddng mdt vai trd quan trong trong cic xi nghiep det may. Trong cic hi thing kiim soit chit Iwgng truyin thing, ky thuit vien phai phit hien vi phin loai ldi vii det mdt cich thu cdng. Cdng viec niy tin kim vi nhim chin. Han thi nwa, do chinh xic phu thudc vio kinh nghiim cua ky thuat vien va do chinh xic niy thwdng giam sau nhiiu gid lim viic. Di giii quyit vin di dd, trong mdt vii nim gin diy, mdt si he thdng kiim soit chit Iwgng tw ddng di/a tren thi giic miy tinh vi miy hoc di dwgc di xuit Di cd thi img dijng trong mdi trwdng thwc ti, dc he thing cin thda man hai yiu ciu: do chinh xic cao va hoat ddng trong thdi gian thwc. Trong bai bio niy, chung tdi gidi thiiu mdt phwong phip phit hiin ldi vii det sw dung bd lgc Gabor Phwgng phip niy cho kit qui rit khi quan.

I. INTRODUCTION

Fabric inspection plays an important role in garment companies. With conventional fabric inspection svstems, the human inspectors have to detect and classify defects manually.

This work is tedious and expensive. Moreover, defect detection accuracv depends highly in inspector's experience and decreases after a number of working hours. Experimentations show that even experts cannot detect more than 60% of the overall defects if the fabric moves faster than 30m/min or wider than 2m [I]. In recent years, several automatic fabric inspection approaches based on computer vision and machine leaming techniques have been proposed [2]. In order to employ these approaches in real application, they have to satisfy two main requirements: high accuracy and real-time. In this paper, vve present a method for defect detection based on Gabor filter that meets these requirements.

The remaining sections of the paper are organized as follows. In section 2, we analyze the related works. Section 3 presents our method for defect detection. The experimental results and discussions are given in section 4.

Finally, the conclusions from this work are summarized in Section 5.

II. RELATED WORK

Many works have been made for fabric defect detection. These approaches have been based on three different approaches; statistical, model and spectral based. In statistical approach, gray-level texture features extracted from cooccurrence matrix [3], The model-based techniques such as Vlarkov random field (MRF) can be used for defect detection. There also exists many spectral approaches for fabric defect detection. For example, in [4] the authors proposed a method for defect detection using Gabor filters which needs a large amount of computations. In this paper, we present a fast defect detection method based on Gabor filter.

JOL RNAL OF SCIENCE & TECHNOLOGY * No. 83B-201I

III. PROPOSED FABRIC DETECTION APPROACH

DEFECT 3.1. Odd symmetric Gabor filter

Defect detection is the first and crucial step in anv fabric inspection systems. The aim of this step is to determine the present of defects in textile images. Figure 1 depicts fabric defect detection method using Gabor filters.

Firstly, the input image is filtered by two kinds of Gabor filters (odd and even symmetric) vvhich allow to highlight edge-shape (odd svmmetrie Gabor filter) and blob-shape (even symmetric Gabor filter) in textile image. Then, two images generated by applying two kinds of Gabor filter on input image will be fused.

Finally, the post processing containing smoothing process and binarization will run on the fused image (to further reduce the amount of noise, and to discriminate defective texture pixel from non-defective texture pixel). In the following section, we describe in detail the steps of this method.

mage (IM)

Oodd= |IM*Odd_GW|

|IM*Even_GW|

Image Fusion

Smoothing Filter

Binariation

Figure 1. Block diagram oflhe defect detection using Gabor fillers

The rest of this section, vve will propose method which improve the time of algorithms using theory of Circular matrix and FFT DEFT operation.

Odd Gabor filter is a multiplication of Gaussian filter and sine function;

g,(x-v) = -esp<-- >sin(27TW^\ 1

Where o \ and a\ refers to the radial frequency bandwidths of the Gabor wavelet, co\

denotes the central frequency, (o\ = \I(\I2-K a\) and (x",y') are the (x,y) coordinates related by 6' and translated by (fs.t\) in accordance with the equation;

cos9' -sinG' sinO' cos9' V - 1 ' ,

An odd symmetric Gabor filter (Oodj) perfonns well in detecting edge-shape fabric defects including mispick, overshot, etc. Figure 3 shows the result of odd Gabor filter for the defect image in Figure 2.

Figure 2. Defect in textile image

Figure 3. Result image of odd Gabor filler for defect image in Fig 2

3.2. Even symmetric Gabor filter

Even Gabor filter is a mutilplication of Gaussian filter and cosine function:

,(v.v) = -evp ' C ( l s l J - o , V )

JOLRNAL OF SCIENCE & TECHNOLOGV * No.1i3B-^lOtl

.-\n even symmetric Gabor filter is good at detecting blob-shaped fabric defects including knot. burl. etc. The resuh image of even Gabor tlher for defect image in Figure 2 is shown in Figure 4.

Figure 4. Result image ot even Gabor filter for defect image in Figure 2

3.3. Image fusion

In order to reduce the false alarm rate and at the same time maintain the high probabilitv of detection of defects, the output images from two Gabor filters need to be fused. A varierv of image fusion methods have been developed to help attenuate the background noise and accentuate the pixel indicating the defective areas [5], which help attenuate the backgroud noise and accentuate the pixels indicating the defective areas, A commonly used image fusion method is used in [2]. In this paper, we use this method for image fusion. With this method, two Gabor filters O^dj and Oe^en are nomalized to [0,1] by using:

0 4x,y) = 0,(x.y)-min(03(x,y)) ma.x(0, (X, y)) - min(0^ (x. y))

(a = odd or ev en)

Then, the resuhing output image F of the fusion step can be generated by using the equation:

F(x,v) = l - ( l - 0 „ „ ( . x , v ) ) x ( l - 0 „ , , . ( x , v ) ) or

H^y)=0^,(xy)+0^(x.y)-q,„.(x.y)xQ^(xy) The output image F is prone to follow closelv one input image if the gray levels oflhe pixel in the other input image are low [6].

3.4. Post-processing steps

\ Gaussian low pass filter is used as the smoothing filter for reducing speckle-like noise in resulting output image F for the fusion step.

We have output image B lor the smoothing

step. In the binarization step, the output binai) image D is computed as:

1 B(x.yl>>L„3^, or B(x.v )<>.„,„

D(x.y) =

0. ^ . , n ^ B ( x . y ) < ^ , , , W here D(x.y) is the binary value of the image pixel in the position (x.y) of the image D.

The thresholding limits X,„.,^ and Xm,„ are determined as follows;

l>-.as=nia.x|B(x.y)|

[X_=min|B(x.y)i with (x.y) e W

Where W is a sub-window centered at the image B. obtained by filtering a defect-free template image using fvvo Gabor filters and the smoothing filter. The size of which should be suitably chosen to avoid the distortion effort caused bv the edges of the image.

Figure 5. Result image obtained bv using the proposed method. White pixels represent lextile defect.

3.5. Optimization of Gabor Filter

As we present in Figure 1. the defect detection method based on Gabor filter has to compute the odd and even Gabor by using the following fonnula;

0«

Oe,

IM*Odd_GW|.

:iM*Even_GW|,

.As we can see. the computation of odd and even Gabor filters requires to perfonr frequency convolution operation. Therefore, the computation time is consideration especiall) when working vv ith big-size image. In order tc improve the computation, vve propose to use th(

circular convolution and FFT/DFFT.

The convolution c * x = b can be dom by a different way;

F.(c'.x) = F„(c)F„(.x) = F„(b)

JOI RNAL OF S C I E N C E * TECHNOLOGY * No. 8 3 B - 2 0 I I

with b=F;'(F„(c)F„(x))

Therefore, the odd and even Gabor filters can be computed as;

o„dd = f;:'(F„(iM)F„(o„,,_Qw))

0 „ „ = F ; ' ( F „ ( I M ) F „ ( 0 _ ow))

Where F/F'' is FFT/DFFT operation. IM is input image. 0„;id ovi', Oevc„ cw is Circular matrix vvhich have the same dimesions of the input image. Oodj ow, Ocven ow are Gabor filter masks.

IV. EXPERIMENTAL RESUUTS AND DISCISSIONS

The performance of the proposed defect detection scheme is evaluated by using a set of 74 defect images collected in Norlfolk Hatexco company, VietNam. The defect images are captured by using a normal camera. There are fwe textile defects: holes, dirty mask, vertical/horizontal line, loose yarn and thicker varn.

In the implementation, we have chosen filter parameters as follows: a^ = 4, a = 4 ,

0 = n / 4 , 0), The non defect image are used to compute value of ^.^.i^ and X,„,„ (e.g.

for dirty mask image, the defect - free image determine X,,,,^ = 0.99 and X,n,„ = 0.3).

^^MBBJI

^ m ^ l ^ T A I ^

l ^ r T ^ ^ ^

KisBMn^fln

Si^Sift.'^

^m

^ S ^ k ^ L j

Figure 6. Example of hole lextile defect

Figure 8. Dirty mask defect

Figures 9, 10, 11 illustrate defect detection results. As we can see, defect regions are detected and localized. Hcvwever. in some case (see Figure 10), Gabor filter can not allow to distinguish defect zone and non defect zone.

We compared the computation time of our proposed method (that uses FFT/DFFT operation) with that of conventional method with different sizes of image. For this comparison, we use a computer with Core2 Quad ff'2.83Ghz CPU and 2.0G R.'VM.

Figure 9. Defect detection resull of hole defied image

Figure 10. Defect detection resull of Vertical 'horizontal line defect image

Figure '. Vertical horizontal line delect

Figure 11 . Defect deleclion result of dirty mask detect image

J O L R N \ L OF SCIENCE* TECHNOLOGY * No. 8 3 D - i u r t

V. CONCLUSIONS

In this paper, vve have presented a method for defect detection based on Gabor filter. The experimental results shows that this method allow to detect effectively and efficiently textile defects in images.

Table 1. Computation time of two methods Image size

512x512 1024x1024 2048x2048

Our method 0,3 s

1.2 s 3,0 s

Conventional method

2.4 s 9,0 s 18s However, our work has still two problems. Firstly, based on experimental

results, vve see that using only Gabor filter can not allow us to detect all defects of interest (see Figure 10). Other features such as LBP (local binary pattem) should be used and combined with Gabor filter. Secondly, our experimental resul is still limited. We intend to evaluate our work with a more complete database.

Acknowledgement:

The research leading to this paper was supported bv national project, grant number 0IC-02/0I-20I0-2, entitled "Research and development of an automated fabric defect detection system based on image processing and recognition techniques'

REFERENCES

1. Cho, C.-S., B.-M. Chung, and M.-J. Park, Development of real-time vision-based fabric inspection system. IEEE Transactions on Industrial Electronics, 2005. 52(4): p. 1073-1079.

2. Sari-Sarraf H. and J.S. Goddard, Vision system for on loom fabric inspection, IEEE Trans Ind Appl 1999.

3. Tsai, I.-S., C.-H. Lin, and J.-J. Lin, Applying an artificial neural network lo pattern recognition in fabric defects. Textile Research Joumal, 1995. 65(3): p. 123-130.

4. Beirao, CL. and M.A.T. Figueiredo, Defect Detection in Textile Images Using Gabor Filters in Image Analysis and Recognition Lecture Notes in Computer Science. 2004. p. 841-848.

5. Casasent, D.P., Gabor wavelet filters and detection, in Proceedinas of the SPIE. 1995.

fusion for distortion-invariant multi-class object

Sari-Sarraf 11, Goddard JS. Vision systems for on-loom fabric inspection. lEEl-; Trans Ind Appl 1999;35; 1252-97.

Author's address: Le Huy Viet - Tel (^-84) 98 4648 798 - Email; Huy-Viet.Le@mica.edu.vn Intemational Center MICA, Hanoi University of Science and Technology No. I, Dai Co Viet Str., Hanoi, Vietnam.