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Defects in fabric have been and continue to be a major source of seconds in finished garments. These defects persist despite several visual inspections and intensive efforts to remove defective parts during sewing operations. The increased use of automation in assembly steps will intensify the problem of detection and removal of fabric defects in cut‐parts. Describes a workstation utilizing machine vision which has been designed and constructed to detect and remove defective cut‐parts prior to the initiation of assembly operations. The workstation employs two vision systems — an area camera and a line camera — to inspect parts on a conveyor belt both statically and dynamically. The colour of the parts is also determined and the area and perimeter are measured to detect improperly cut parts. The acceptable parts are then stacked in a manner suitable for input to an automated sewing station. The workstation should permit placing into the assembly operations a set of defect‐free, properly‐cut and colour‐matched parts. It is estimated that this cut‐part inspection system will reduce defects in finished garments by approximately 50 per cent and should greatly simplify the labour‐intensive and costly fabric defect control systems currently in place in most apparel plants.

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

Inspection of fabrics is a major consideration in fabric manufacture, as well as in manufacture of garments and other fabric-based goods. In this research, a computer-based system for objective assessment of fabric defects was designed with emphasis placed on fabric defects occurring in the Sri Lankan industry. Image processing techniques were used to analyse scanned images of the test fabric, compare it with an ideal sample, and identify defects according to pre-learnt rules. The information gathered was then used to grade the fabric, either by determining the frequency of defect occurrence or assigning points.

A new classification method for common defects was designed, thereby facilitating grading according to commonly used grading systems. A coding system for defects was also designed to help report defects to the user. The fabric defects were classified and stored according to the developed classification method and coding system.

In recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule generation. The purpose of this paper is to propose a real-time fabric inspection technique. This work deals with the multi-class classification of fabric defects using rough set theory.

This technique focuses on the classification of fabric defects using the effective decision rules envisaged by rough set theory. In the proposed work, the six features of 50 images have been used for multiclass classification of fabric defects.

In this work, 40 images were used for generation of decision rules and 10 unseen images were used for validation out of which nine images are accurately predicted by the proposed technique.

The proposed method accurately identified 9 out of 10 testing defects. The obtained decision rules provide an insight about the classification method which ensures that the prediction accuracy can be improved further by framing more robust decision rules with the help of a large training data set. Thus, with the support of modern computational systems this method is potent in getting recognition from the textile industry as a real-time classification technique.

Recent developments in the hardware and software mean that the automation of visual fabric inspection tasks is becoming feasible at low cost. This paper investigates the techniques that can be used to solve the problem of repetitive, tedious and physically demanding human inspection for defects in shirt collars. The faults studied in this work are those found in nine types of defects that can be present on shirt collar panels. Two statistical methods: moving group average, and moving divided group average are proposed. In addition, highlighting and variance techniques are applied to an image with moving group average and signature counting. These techniques gave an indication of fast computation time to detect the defects on the image, which is needed in manufacturing, and could be applied to most automated inspection systems.

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.

In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.

Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Low‐stress mechanical properties of fabrics are very important in many applications as well as in manufacturing process control. Discusses the importance and potential applications of an on‐line mechanical property measurement system. In addition, the working principles of existing off‐ line fabric bending testers have been critically reviewed. It is suggested that the principle of a future on‐line system to evaluate fabric bending behaviour should be based on the characterisation of fabric loop shapes.

Lack of trust and poor quality of construction deliverables have become a serious matter nowadays. This is due to the absence of a uniform and decentralized system for managing quality information. In Kuwait’s industry, many incidents have been recorded as a lack of confidence in the authenticity and integrity of the documented data in the system. This paper aims to shed the light on a framework that would tackle this matter.

A designed framework using Blockchain technology (Hyperledger Fabric) has been used to create a transparent and decentralized environment between the parties. A digitalized informative checklist referred to as “Smart Construction Inspection Checklist (SCIC)” has been initiated to enhance the poor information recorded between the parties.

The framework has provided a transparent, immutable, traceable and decentralized environment in which all parties are involved in transactions. In addition, the integration of the SCIC in the blockchain environment provided an advantage in which all the necessary criteria of inspection will be stated, checked by the consultant and validated by the client to approve the transaction. A preliminary testing has been conducted to support the proposed framework.

This study fulfils the gap in the state of art for further studies to practically apply the framework that will enhance the quality of information management in Kuwait’s industry.

The problems of pattern cutting as applied to flexible elastic mesh fabrics (lace) are described within the context of the total manufacturing process. While the design and knitting stages of lace manufacture are highly computerised, providing associated benefits, the cutting room operates with conventional, slow, labour intensive machinery, leading to substantial processing bottlenecks and dependent costs. A new system is presented which uses machine vision to determine the required cutting path on the lace fabric in real‐time via sophisticated, yet high speed, image processing algorithms. The determined cutting path data are used to direct a high speed CO₂ laser beam to the correct cutting point with beam velocities of typically 6 m/sec. Simultaneous dual edge cutting is now possible using this new system, leading to lace throughput being increased by a factor of ten typically, with the possibility of processing more sophisticated designs and achieving higher cut edge quality.

The purpose of this paper is to develop mathematical relationships to calculate the loop length to knit compact plain knitted fabrics and to validate the model using the fabric parameters of commercial fabrics.

Ellipse defines the shape of the head of a knitted loop and straight lines define the arms of a knitted loop. The mathematical relationships developed relate the yarn count to the loop length of compact knitted fabrics. The experimental data and the data from previous similar research validate the accuracy of the mathematical model.

The model can calculate loop lengths to knit compact plain knitted fabrics in terms of thickness of the yarn and the coefficient defined to express the ratio of minor axis to major axis of the ellipse that defines the shape of the head of the loop. The mathematical model can deliver several loop lengths to produce compact plain knitted fabrics for different values of this coefficient. For commercial fabrics the error of the model was 0.53%.

The present model defines the head of the loop as an ellipse. The uniqueness of the present model is that several ellipses can exist for any given yarn thickness for a range of values assigned to the minor axis of the ellipse. The accuracy of the model against experimental data ascertains that the model is closer to the reality for commercial fabrics and proves the uniqueness of the model. Further, this model is an ideal and a simple model to introduce knitted loop configurations in teaching knitted fabric geometry.