A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

Published ArticleThe dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type

Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

The dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type

An Analysis of Remote Sensing Data to Evaluate the Problem of Atmospheric Aerosol Pollution in Africa

The particulate matter (PM) directly endangers the human health. Remotely sensed tiny atmospheric particles, aerosols, are presented in this research as atmospheric air pollutants. Globally overviewed for the first instances, and then a focus put on Africa and Asia, the selected aerosols are fine particulates (PM2.5), black carbon (BC), and Sulfate (SO4). According to the existing literature, the motivation to research on air pollutants came from the fact that the polluted air globally kills many people, by attacking cardiovascular system. The online accessible remote sensing’s data has been mostly collected from the second version of modern era retrospective analysis for research and applications (MERRA-2), a model selected for its update as well as the fact that its data are directly assimilated from the most renown remote sensors: Moderate resolution Imaging Spectroradiometer (MODIS) and the advanced very high-resolution radiometer (AVHRR). MERRA-2 also compiles data from different aerosol robotic networks (AERONETs). With a vast region of interest, and considering the big temporal resolution, reduced spatio-temporal resolutions facilitated the focused research. Goddard interactive online visualization and analysis infrastructure (GIOVANNI) bridged our research objectives with the data; Geographical Information Systems (Arc GIS) is a main software tool. Map-based as well as time series results for PM2.5 and other atmospheric air pollutants are presented; health dangers associated with the dust from erstwhile research highlighted. Finding that the annually-averaged mass concentration of the dust’s PM2.5 is significantly greater than the mean recommended concentration, 25 μg/m3, in all the seasons of the center of the research region of interest (Africa), this research recommends further research on dust aerosols mitigation strategies, during the seasons of heaviest air pollutants in particular

Conception et réalisation d'un montage didactique pour l'enseignement de l'automatisme industriel

Le présent mémoire est la présentation d'un travail de conception et de réalisation d'un montage destiné à servir de support didactique à l'enseignement de l'automatisme industriel dans une université africaine. Après avoir décrit brièvement les éléments constitutifs des automatismes, l'auteur présente toutes les étapes qui ont conduit à la réalisation dudit montage. En dernier lieu il passe brièvement en revue les autres applications réalisables à partir du même matériel

Graphene-based electrodes for ECG signal monitoring: Fabrication methodologies, challenges and future directions

AbstractElectrocardiogram (ECG) is the most common and simple technique to diagnose cardiovascular diseases. Cardiovascular diseases can be detected effectively if ECG signals are monitored for a long time, producing innovative clinical outcomes to diagnose and treat cardiovascular diseases. Due to skin irritation and degradation of signal quality with time, traditional wet electrodes are unsuitable for long-term ECG monitoring. Researchers are trying to fabricate flexible, wearable, highly conductive and lightweight ECG sensors, which can be applied for long-term monitoring of ECG signals and the detection of several cardiovascular diseases. Graphene is used for fabricating dry ECG electrodes because it exhibits robust mechanical flexibility, good environmental stability and excellent carrier mobility. This review paper presents the progress of various fabrication methods to make graphene-based ECG electrodes and provides the researcher’s clarification on recent advancements and direction in this domain. This paper focuses on a systematic review and comparative study of various fabrication methods of graphene-based ECG electrodes, such as screen printing, dip coating, drop casting, wet transfer, electrospinning, wet transfer and dry patterning, spin coating, spray coating, ink-jet printing etc

From theory to practice: Understanding DevOps culture and mindset

AbstractDevOps is a potential solution to time and quality restrictions in software development. It aims to increase the frequency, quality, and speed of software delivery using automated procedures. DevOps is a culture transformation, not just a toolchain. DevOps emphasizes cooperation, automation, measurement, information sharing, and web service utilization. It positively impacts IT development, online services, and quality assurance. Before commencing the DevOps journey, it is necessary to understand DevOps principles, practices, tools, benefits, and underlying issues. Such vital parameters are critically reviewed in this article. This systematic review addresses gaps and recommendations related to DevOps, aiming to provide a comprehensive understanding of its culture and mindsets. The article presents an in-depth examination of DevOps, covering topics like architecture, components, tools, principles, and security challenges. It establishes a conceptual framework for practical implementation. Security has also been discussed in the paper, which is one of the difficult problems in DevOps implementations. The research findings aid in a better understanding of the phenomenon from a human factors perspective. The state-of-the-art discussion on several tools covering architectural and networking aspects in DevOps is included in this article to attract practitioners and researchers for DevOps adoption. Our analysis revealed three key themes related to DevOps culture and mindset: collaboration, continuous improvement, and automation. Moreover, DevOps is not immune to challenges. The proposed work presents the existing gaps and future research directions to address the same