Diffractive Optics for Fabric Fault Detection

Diffractive Optics for Fabric Fault Detection A. SPARAVIGNA, G. DORMA DIFIS, Politecnico di Torino and B. MONTRUCCHIO DAUIN, Politecnico di Torino Corso Duca degli Abruzzi 24 I-10129, Torino, Italy

ABSTRACT Several devices for textile fault detection, based on diffractive optics, have been proposed in the past without appreciable influence on direct on-loom inspections. Nevertheless, the use of diffractive optics cannot be considered surpassed by other inspection systems. Compared with procedures based on image processing, it is insensitive to dust and vibrations and does not suffer from the presence of ambient light. We have investigated and improved a method based on the optical detection of the Fourier power spectrum to reveal and identify defects in textile structures. The optical set-up analyses the local behavior of the texture, which is converted in a power spectrum. This system can be easily inserted in an automatic inspection device, which determines the presence of a defect comparing the actual power spectrum with that of the good fabric assumed as a reference. The processing of data is rather simple since it is based on the number and position of peaks in the spectrum. A byproduct of our investigation, is a device to measure the yarn density in the fabric.

Keywords: Diffractive optics, Fault detection, Yarn counting.

1. INTRODUCTION Fabric inspection has proven to be one of the most difficult of all textile processes to automate. In spite of the presence of some commercial automated systems, based on adaptive networks and working with specialized computer processors, most of fabric inspections is still performed by trained staff on offline stations. The problem of quality evaluation during the production process on the web, is then an open

problem to solve, very important for lowering costs and improving quality. Automatic systems based on the artificial vision must perform a very difficult task since faults on fabrics are often very small and hardly detectable, with their visibility strongly dependent on illumination systems (front or back lighting) and reduced by vibrations of the mounting devices [1, 2]. Several statistical approaches to the image texture processing have been developed [3, 4, 5], such as methods based on the Fourier space representations to measure the spatial frequency of the grey tone levels [6]. The Fourier method, that seems the most promising due to the regular structure of the fabrics, suffers from the local intensity variation in the image, that can be easily confused with the true presence of a defect. Use of wavelets was then proposed, getting advantages from the wavelet property to analyze the local behavior of the texture in comparison with chosen grey level distributions [7]. Here, we want to discuss the automatic inspections with opto-electronic processing based on diffractive optics. Several devices, that we discuss in the next section, have been proposed, unfortunately without appreciable influence on direct on-loom inspections. Nevertheless, the use of diffractive optics cannot be considered as definitely surpassed by image processing systems. It is insensitive to dust and vibrations, and does not suffer from the presence of ambient light. We improved an optical method to detect and identify defects in the fabric. Our set-up, discussed in Sect.3, analyses as the wavelets do, the local behavior of the texture and determine the local power spectrum.

This spectrum is compared with that of the good fabric assumed as a reference. The following processing of the data reduces the two–dimensional image to a one–dimensional curve: it is then rather simple to evaluate the presence and nature of defects, checking the number and position of peaks. A byproduct of our investigation, could be a device to measure the yarn density of the fabric. In the following section, we will briefly discuss how the diffractive optics has been applied till now to the fault detection on textile surfaces.

reduced due to a density change in the weaving of the web. The double pick happens when two picks are in the same weaving position of the fabric and it is rather difficult to detect.

2. DIFFRACTIVE OPTICS FOR FAULT DETECTION The Fourier spectrum of a light beam passing through a lens is formed in its focal plane. If a transparency, on which an image is reproduced, is placed in front of the lens and lightened with a coherent light beam, we will find on the rear side of the lens at the focal distance the power spectrum of the image. This intensity distribution can be easily detected with a photographic plate or a CCD camera. A relatively simple optics can then be developed to evaluate the optical power spectrum of the Fourier transform of an image or for convolution of two images [8, 9, 10, 11]. These properties of optical systems have interesting applications in those devices where the check of the regularity of an image texture is required. In the case of fabrics, for instance, where defects are deviations from the regular weaving of the yarns. The use of diffractive optics in industrial applications was introduced for the first time in 1960 by Lendaris and Stanley to analyze transparent materials [12]. Many other applications were then proposed with more or less good results. Harvey L. Kasdan was one of the first researchers to study diffractive systems, with several devices for industry, in particular for paper quality evaluation and for the detection of defect in textile fabrics [13]. He studied how to find the most common defects in cotton fabrics, the missing yarn, the double pick and the density change in the weaving process of the web. Briefly, a missing yarn happens when a yarn breaks in the weaving mechanism of the web. A consequent deviation from the regular geometry of the fabric is produced, since the distance between yarns is changed. As shown in Figure 1 on the left, the lack of a yarn gives a quite visible defect. Another defect shown in the same figure, on the right, is produced when the spacing among yarns is

Figure 1: Defects in a fabric. On the left, the defect produced by a missing yarn, on the right the product of a density change in the weaving process. The image size corresponds to 3 cm.

Figure 2: Power spectrum of the fabric without (a) and with double-pick defect (b).

The work of Kasdan shows that it is possible with an optical Fourier transform (OFT) to detect the defects. The power spectrum of the fabric with a defect is different from that of the good fabric: peaks changes in intensity, shape and position. The Fig.2 is obtained by placing in front of a lens, a piece of regular fabric and a piece with a double pick: as observed by Kasdan, there is a splitting of some peaks in the Fourier transform. To have an automatic evaluation of changes in the peaks, Kasdan used an array of photo-detectors. C.H. Chan and G. Pang obtained the same results on the spectrum peaks recording the image of a regular fabric and the image of the same fabric with a double yarn and analyzing them with an image processing [6]. Let us note that

the success of this procedure with image processing in detecting defects is strongly depending on the resolution and contrast of the images. After the fundamental work of Kasdan on the detection of defects in fabrics with Fourier optics, S.Ribolzi et al. proposed a system for detection of faults directly on the web: the system allows the evaluation of the number density of yarns and the local mean diameter of the yarns [14]. Castellini et al., in their device for fabric inspection, prepared a mask with the OFT of the regular fabric [15]. The fabric with defects has an OFT different and with noise among peaks. Then the mask is used to remove the regular peaks and to show only the noise among peaks. This procedure has a difficulty: each time that the web is producing a new fabric, a new mask is required. All these researches are very interesting but they are not exhaustive. As we have seen, there are several methods that can be chosen to evaluate the presence of a fault, but sometimes are difficult to realize or are not able to identify the defect, that is, if it is due to a missing yarn or to a double pick. In the case of the system of Castellini et al., for instance, with the use of a mask it is only possible to tell if there is a defect or not. We shall see in the next section how it is possible to have an identification of defects displayed by fabrics with the use of stops on the laser beam. We then do not need to investigate the presence and the amount of noise among peaks but simple the position and shape of the peaks. According to peak changes, we can identify the nature of the defect.

Figure 3: Optical set-up. 1 is the laser lens and 2 the beam expander. Filter 6 is placed between 5 and 7, to remove ambient light. 3 and 4 are mirrors. On the CCD, the power spectrum of the fabric placed between the two mirrors is formed.

Figure 4: Picture of the experimental set-up.

3. EXPERIMENTAL SETUP AND RESULTS The experimental set-up used for textile fault detection is shown in Fig.3. The system is composed with a diode laser in front of a beam expander, two mirrors and the optics for the Fourier transform. The power spectrum is formed on the detector of a CCD camera. Between the two black bodies shown in Fig. 4 it is placed the fabric sample. The intensity of the beam can be adjusted with a polarizer. The light source is a laser-diode with wavelength at 670 nm. The beam expander gives a light beam with a diameter of 30 mm. A stop with an adjustable diameter is place after the beam expander: it is used to change the diameter of the light spot falling on the fabric. The Fourier optics is composed of two lenses and has a focal length of 170mm. In Fig. 3, optics 1 is the laser lens and 2 the beam expander. A filter (element 6) is placed between elements 5 and 7 of the Fourier optics, to remove ambient light. 3 and 4 are mirrors. On the CCD, it is created the power spectrum of the fabric that is placed between the two mirrors. It is

necessary to observe that, with the recording by means of CCD camera and computer, we have not the precise spectrum coming from the Fourier optics, but, due to setups of camera shutter and frame grabber, used to capture and record the images, the recorded intensity is slightly different. For an object, located on a plane ( x , y ) in front of the Fourier optics, at a distance do and illuminated by a normally incident plane wave of amplitude A ,

and a function f ( x , y ) representing the transmission of the object. λ c is the wavelength and f c the focal distance of the Fourier optics. The Fourier Transform relation between the object brings to an intensity distribution across the focal plane given by: I (x , y ) =   

∫∫

A2

λ

2

f c2

 k  d  1 − o  u 2 + v 2 exp  j  f c   f c 

(

   2π (xu + yv ) dxdy  f ( x , y ) exp  − j λ fc   

the number of the peaks is reduced, only the main peaks remains, and the noise among peaks is suppressed. This could seems to compromised the fault detection, but in fact, it is not so: the use of the stop greatly improves the fault detection. If we analise a region of the fabric with a defect, we see, as shown by images reported in Fig.6, that the defect produces a change in the peaks position.

) × 

2

(1)

where (u , v ) are the coordinates in the focal plane. The wave-number k is 2π / λ . The frequencies in the power spectrum are given by the following expressions: fx =

u λ fc

; fy =

v λ fc

(2)

The complex function f ( x , y ) can simply represents a modulation in the intensity of the light passing through the material; in the case of pure absorption, the function can be posed as real and used to model fabrics, as well as paper or nonwovens. The Fourier spectrum image is transferred from the CCD to a computer and visualized on a monitor. The image of the Fourier spectrum can be further elaborated. In fact, the detection of defects from the Fourier image as those shown in Fig.2 is rather complex, and then not suitable for on-loom inspection. At the authors’ knowledge there is only one optical commercial system performing convolution between the good spectrum and the spectrum with defects [16]. Recently, in Ref. [17,6], the researchers proposed to use stops to reduce the laser beam falling on the fabric to increase the signal of defect compared with the signal obtained in the case of good fabric. These researchers, in fact, obtained an increase of the fault signal but surprisingly, they do not mention what we observe with our experimental set-up and that report in the following. For the fabric shown in Fig.1, we put the stop with a diameter of 3.5 mm. A smaller diameter should lower the signal too much. Figure 5 shows how the power spectrum is changed if the diameter of the beam is reduced from 30 mm to 3.5 mm. First of all,

Figure 5: The power spectrum changes if the diameter of the beam is reduced from 30 mm (a) to 3.5 mm (b). The number of the peaks is reduced, only the main peaks remains, and the noise among peaks is suppressed.

In Fig.6 (a), it is shown the power spectrum of the good fabric on the left, and on the right the spectrum of the fabric with a missing yarn: the vertical distance among peaks is reduced (the image was rotated for convenience). To explain this result, let us consider that the diameter of the beam is comparable with the size of the region occupied by the defect. In Figure 1, the region indicated by the arrows has a size of 3 mm: there, the distance among the yarns is increased since one yarn was missed in the weaving process. In Fig.6 (b), the spectrum of a double pick is displayed: as observed by Kasdan, the signal is reduced and the lateral peaks split. The position of the peaks is not substantially altered since the double pick is a defect that does not alter the position of the surrounding yarns. In Fig.6(c), it is the power spectrum of the defect shown in Figure 1 on the right: it is a region of the fabric where the number of yarns is more dense than in the surrounding region

and it is due to a change in the weaving process of the web. The distance of the peaks in the power spectrum is then larger than that for the good fabric.

Figure 6: In (a), the power spectrum of the good fabric on the left, and on the right that of the fabric with a missing yarn: the vertical distance among peaks is reduced. In (b) on the right, the spectrum of a double pick, the signal is reduced and the lateral peaks split. In (c), the power spectrum of a density change in weaving. The distance of peaks is larger than that for the good fabric.

The images of the spectrum that we have obtained are now easy to be processed. With the use of a proper stop we suppress the noise among peaks, that is no more important for the detection of defects, since we base our processing on the position of the peaks and on the shape of them, as follows. The image is converted in an array with a grey tone associated to each pixel. The sum of the grey tone of pixels belonging to each row is made and curves are obtained as those shown in Fig. 7 and 8. These curves are one- dimensional signals corresponding to image frames of the fabric. The signal changes if a defect is displayed in the fabric. In Fig.7, the signal for good fabric (broken line) is compared with the signal of a defect, that due to a missing yarn. The peaks are closer: the distance is reduced of 30%. In the following figure, the density change produces an

increase of the distance among peaks. A defect can then be revealed and identified from the position and shape of peaks in the one–dimensional curve, as we have checked with other defects and different weaving structures of fabrics.

Figure 7: The signal of good fabric (broken line) is compared with the signal of a missing yarn. Peaks are closer of 30%.

Figure 8: The density change in weaving produces an increase in distances among peaks.

4. CONCLUSIONS With the use of the proper stop, we increased the signal of the defect, compared with the signal of the good fabric. The corresponding spectrum images can

be easily processed, converting the two–dimensional grey-tone map of the image in a one– dimensional curve. From this curve the distance of the peaks is deduced: a change in this distance gives information on the nature of the defect appearing in the fabric. We are studying an analog system to convert the video signal in an audio signal, performing then the convertion we are actually making with the image processing. The Fourier optical system with an analog video– audio converter could be rather interesting as a counter for yarns, placed in a fixed position on the loom.

5. ACKNOWLEDGEMENTS The work was partially funded by Incas S.p.A., Vigliano Biellese, Italy.

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