Leather features selection for defects' recognition using fuzzy logic

Krastev, Kaloyan; Georgieva, Lidia; Angelov, Nikola

doi:10.1145/1050330.1050367

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Poelzleitner and Niel followed a hierarchical approach and was able to detect seven features of wet-blue leather [20]. Krastev et al showed a histograms-based detection method, using the χ 2 criteria for image analysis and histogram construction [21]. This method detects leather defects based on evaluating the distinction between the grayscale histogram and other image search areas.…”

Section: Miscellaneous Methodsmentioning

confidence: 99%

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

et al. 2019

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Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.

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Section: Miscellaneous Methodsmentioning

confidence: 99%

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

et al. 2019

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“…Villar et.al. also introduced an automated computer vision system to detect a few types of defect (e.g., closed cut, fly bite and open cut) [21]. Seven popular feature descriptors (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

et al. 2020

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Leather defect analysis is important for leather quality grading which directly effects the leather exports. Automated leather sample classification is vital due to slow and subjective nature of the manual process. The major challenges that exist in visual inspection of leather samples for categorization are: the morphology of defects significantly differs and their close examples are not available for transfer learning, unavailability of publicly available data and a benchmark. In this paper, we discuss three important aspects in the identification of industrial leather defects, i.e. the creation of an annotated wet-blue leather image dataset, the transfer of information from different domains to the leather image domain and the design of ensemble networks tailored to the task. We are therefore introducing a new database of wet-blue leather images (Wet-blue Leather Image Dataset (WBLID)) for the classification of defects along with expert annotation data. We proposed a new network EfficientNet-B3+ResNext-101. The proposed EfficientNet-B3+ResNext-101 ensemble architecture significantly outperforms all other state-of-the-art methods in terms of AUC and F1-score.

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“…Image textures have been introduced into a wide range of applications such as textiles inspection [8][9][10], cell recognition and counting [11], ultrasonic image processing [12], food quality evaluation [13], and inspection of wood [14], paper [15], and leather [16]. In manufacturing, it is well known that surface quality is affected by many parameters including tool wear, vibration level, and machining conditions such as spindle speed, feed, and depth of cut.…”

Section: Introductionmentioning

confidence: 99%

Application of computer vision for the prediction of cutting conditions in milling operations

Gadelmawla

Eladawi

Abouelatta

et al. 2009

Proceedings of the Institution of Mechanical Engineers, Part B:

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Machining conditions such as speed, feed, and depth of cut significantly affect tool wear, which in turn affects the surface quality and thus this is an area of research interest. With the growing emphasis on industrial automation in manufacturing, vision techniques play an important role in many applications. One of these applications is texture analysis. Although this technique has been extensively researched it has only infrequently been used to predict the cutting conditions of machined surfaces. This paper introduces an application of computer vision to predict the cutting conditions in milling operations (feed, speed, and depth of cut) using grey-level co-occurrence matrix texture features. A software, named the Cutting Conditions Prediction in Milling has been developed in order to predict the cutting conditions from the captured images of machined surfaces. Three modules were developed to perform the prediction process and they are presented in this paper. The system was verified by predicting cutting conditions for various specimens and the maximum error between the predicted and the actual cutting conditions did not exceed ± 10.6 per cent.

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Leather features selection for defects' recognition using fuzzy logic

Cited by 12 publications

References 10 publications

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

Application of computer vision for the prediction of cutting conditions in milling operations

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