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In spite of the modern weaving machines in the industry many defects that occur in fabric are in weaving process and those defects are still defected from human eye. A machine vision can be adapted which detects defects in weaving machines is proposed in this paper.
The disfigured areas which harm the appearance and execution of a fabric might be known as 'a fabric defect'. In modern weaving trchnology, numerous sorts of fabric defect still happen, The reasons of which are many, for example, the yarn quality utilized in weaving, its r weaving mechanics.
The defects experienced inside production must be distinguished and remedied at the beginning phases of the production cycle. Most defects emerging in the production cycle of a fabric are as yet identified by human inspection. A woven texture bar is put on the texture examination machine and twisted from the back to the front pillar while disregarding an enlightened surface. The quality control staff needs to filter almost 2 meters width of texture (Figure 1) and must identify little subtleties that can be situated in a wide zone that is traveling through their visual field.
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The identification and classification of these defects are time consuming and tiring procedures.
In the best case, a quality control individual can recognize close to 60-70% of the defects present, and can't manage a texture more extensive than 2 meters. It is seen that the review speed of a texture woven with a productivity of even 97% is 30 m/min, and just about 60% of the defects are recognized
The issue is to plan a machine vision framework for fabric inspection machines and build up a calculation for fabric defect discovery naturally.
The framework will be convenient, and effectively versatile to a wide range of fabric examination machines. Fabric producers can adjust this framework by utilizing their current fabric assessment machine. The automated defect detection system will upgrade item quality and result in improved efficiency to fulfill both client demands and decrease cost
Related with off-quality. The employment cost will be decreased by replacing human vision identification with programmed visual recognition, where texture imperfections will be assessed equitably. An information base of the imperfections can be framed and they would then be able to be dissected all things considered. Thusly measurable assessment of denim quality can be accomplished. The input information obtained from the programmed review framework may gives the creation of great fabric with less defects in a shorter time.
Survey on image processing techniques numerous attempts are made to replace traditional human inspection by automated visual systems using camera nodes image processing routines. various kind of image processing methods such as gabor filters, wavelet analysis, and the fourier Transform have been applied for fabric defect detection. artificial in- telligence (AI) methods such as the neu- ral Network (NN), fuzzy Logic (FL) and fenetic Algorithm (GA) are also used for defect detection and classification studies. Studies are commonly performed off-line using digital images . Sample images are obtained by using different types of Image capturing devices or from a defective fabric image database. There are fewer studies on real-time defect detection than offline ones. Celik et al. suggested two different de- fect detection algorithms based on the wavelet transform and linear filters using the same system presented in this study.
Karayiannis et al. suggested a pilot system for fabric defect detection and classification in real-time. Double thresh-olding, binary filtering, binary labelling, multiresolution rotting via the wavelet transform, and statistical fabric feature removal methods were used for image analysis and defect segmentation. Eight types of classification were performed: no error, black vertical error, white vertical error, wrinkle, black hori- zontal error, white horizontal error, black spot, and white spot. This system was able to differentiate the eight types of defcts with an accuracy of 94%.
Goswami and Datta used morpho- logical operations such as corrosion and opening operations as well as the Fou- rier Transform for fabric image analysis. Fabric images that contain a knot and thick yarn were detected by morphologi- cal corrosion functioning Maketall proposed a model of a real-time computer vision system for detecting defects of textile fabrics. The Gabor function was used for defect division. Both online and offline tests were to be performed using the model sys tem.
Han and Zhang derived one even uniform Gabor filter mask and one odd uniform Gabor filter mask from an efficent Gabor filter. The limit of the efficient Gabor filter were accquired by Genetic Algorithm (GA). It was conclud- ed that even the symmetric Gabor filter was good at detecting blob-shaped fabric defects like knot, and the odd symmetric Gabor filter was adept at detecting edge- shaped fabric defects like miss pick. The efficency of the system expressed is of 95.24% over all the detection rate.
Cho et al. suggested a model system for fabric defect detection. The defects observed by the system were warp float, broken pick, hole and oil spot. It was stated that a nearly optimal recognition rate was obtained for an oily spot and spot. However, the recognition rate for warp and pick float was approximately 80%.
Conci & Proença had compared the results of three image-processing methods such as fractal dimension, thresholding, and edge detection. In this study, the relationship between the optimality of each method and the type of fabric fault were presented. Ten types of faults were observed, all of which were plain weave. Tests were implemented using 100 images of fabrics. The results showed that fractal dimension was the reliable way as it detected correctly all the types of defects. By using edge detection, faults were detected 98% of time while the threshold approach correctly 82% of these defects.
Hu and Tsai observed the best packet wavelet transform to detect fabric defects. The three major effects on the classification rate of fabric defect inspection comprising wavelets with various maximum vanishing moments, different numbers of resolution levels, and different scaled fabric images were observed. Basically Four kinds of fabric defects such as missing ends, missing picks, broken fabrics, and oil stains were used. A back propagation neural network (BPNN) was used to classify the fabric defects.
| Defect Type | Number of Frames | True Detection (TD) | False Detection (FD) | Missed Detection (MD) | Overall Detection (OD) | TD Rate (%) | FD Rate (%) | MD Rate (%) | OD Rate (%) |
|---|---|---|---|---|---|---|---|---|---|
| Defect-free | 227 | 219 | 8 | 0 | 227 | 96.5 | 3.5 | 0.0 | 100.0 |
| Warp lacking | 104 | 88 | 16 | 0 | 104 | 84.6 | 15.4 | 0.0 | 100.0 |
| Weft lacking | 57 | 50 | 6 | 1 | 56 | 87.7 | 10.5 | 1.8 | 98.2 |
| Hole | 86 | 86 | 0 | 0 | 86 | 100.0 | 0.0 | 0.0 | 100.0 |
| Soiled yarn | 78 | 77 | 0 | 1 | 77 | 98.7 | 0.0 | 1.3 | 98.7 |
| Yarn flow | 55 | 46 | 9 | 0 | 55 | 83.6 | 16.4 | 0.0 | 100.0 |
| Water stain | 11 | 10 | 0 | 1 | 10 | 90.9 | 0.0 | 9.1 | 100.0 |
There are many different vision system alternatives which are proposed for real-time defect detection a new fabric defct detection machine design is present. A vision detection system which is easily adaptable to any fabric defect detection machine was developed in our study. Now textile industries can easily afford this system and set it up on their current fabric defect detection machines. By using this system the fabric inspection process can be done in a shorter time and less fabric quality inspection persons are to be needed. Fabric inspection processes can be achieved briskly with elimination of any disputes over the quality of the fabric. The performance of the system is tested on fabric. The defect detection rate can be increased by using high performance computers. Also the size of the defects detected can be decreased by higher resolution cameras and specially manufactured illumination.
Real-Time Fabric Defect Detection Using Machine Vision and AI. (2024, Feb 19). Retrieved from https://studymoose.com/document/real-time-fabric-defect-detection-using-machine-vision-and-ai
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