An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Capturing 3D textured inner pipe surfaces for sewer inspection

Inspection robots equipped with TV camera technology are commonly used to detect defects in sewer systems. Currently, these defects are predominantly identified by human assessors, a process that is not only time-consuming and costly but also susceptible to errors. Furthermore, existing systems primarily offer only information from 2D imaging for damage assessment, limiting the accurate identification of certain types of damage due to the absence of 3D information. Thus, the necessary solid quantification and characterisation of damage, which is needed to evaluate remediation measures and the associated costs, is limited from the sensory side. In this paper, we introduce an innovative system designed for acquiring multimodal image data using a camera measuring head capable of capturing both color and 3D images with high accuracy and temporal availability based on the single-shot principle. This sensor head, affixed to a carriage, continuously captures the sewer's inner wall during transit. The collected data serves as the basis for an AI-based automatic analysis of pipe damages as part of the further assessment and monitoring of sewers. Moreover, this paper is focused on the fundamental considerations about the design of the multimodal measuring head and elaborates on some application-specific implementation details. These include data pre-processing, 3D reconstruction, registration of texture and depth images, as well as 2D-3D registration and 3D image fusion

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet

Automation of painted slate inspection

This thesis is concerned with the problem of how to detect visual defects on painted slates using an automated visual inspection system. The vision system that has been developed consists of two major components. The first component addresses issues such as the mechanical implementation and interfacing the inspection system with the optical and sensing equipment whereas the second component involves the development of an image processing algorithm able to identify the visual defects present on the slate surface. The visual defects can be roughly classified into two distinct categories. In this way, substrate faults occur when the slate is not fully formed or has excess material whilst paint faults describe a slate of uneven colour or gloss level. A key element in successfully imaging the slate surface defects is the illumination set-up. After extensive testing, an effective collimated lighting topology was selected and is described in detail. Imaging the slate surface was challenging because it is dark coloured, glossy and has depth profile non-uniformities. A four component image processing algorithm was designed to detect the range of defect types. The constituent components are global mean threshold, adaptive signal threshold, labelling, edge detection and labelling. Having proven a solution on the laboratory test bed, a prototype conveyor-based inspection system was assembled in order to replicate a factory-style environment. Robustness tests were performed on 400 slates and a 97% success rate was achieved. This thesis is concluded with a discussion on the feasibility of progressing this project to installation on an automated production line

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology