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

Aslam, Masood; Khan, Tariq M.; Naqvi, Syed S.; Holmes, Geoff; Naffa, Rafea

doi:10.1109/access.2019.2957427

Cited by 35 publications

(27 citation statements)

References 66 publications

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“…To date, the literature that carried out the automatic classification or segmentation tasks on the leather pieces is yet limited [1][2][3]. Besides, the experimental data are varied and hence it is difficult to make a fair test of performance to verify the effectiveness of the proposed methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Classification System for Tick-Bite Defect on Leather

Gan

Yau

Liong

et al. 2022

Mathematical Problems in Engineering

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Natural leather is a durable, breathable, stretchable, and pliable material that comes in various styles, colors, finishes, and prices. It is an ideal raw material to manufacture luxury products such as shoes, dresses, and luggage. The leather will be categorized into different grades that are determined by visual appearance, softness, and natural defects. This grading process requires a manual visual inspection from experienced experts to ensure proper quality assurance and quality control. To facilitate the inspection process, this paper introduces an efficient automated defect classification framework that is capable to evaluate if the sample patches contain defective segments. A six-step preprocessing procedure is introduced to enhance the quality of the leather image in terms of visibility and to preserve important features representation. Then, multiple classifiers are utilized to differentiate between defective and nondefective leather patches. The proposed framework is capable to generate a classification accuracy rate of 94% from a collection of samples of 1600 pieces of calf leather patches.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Classification System for Tick-Bite Defect on Leather

Gan

Yau

Liong

et al. 2022

Mathematical Problems in Engineering

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“…1. Therefore, the products made from natural leather tend retain these marks, which in turn, affect the leather grade [1]. At present, the common grading standard is SATRA [2], which contains six grades according to the usable area ratio of leather, and Grade 6 is considered unusable.…”

Section: Introductionmentioning

confidence: 99%

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

et al. 2021

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Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually, and is time-consuming and labor-intensive for the professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques is proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) are developed in this paper by using weighting to suppress background and enhance the contrast between target and background. Experimental results show that the overall performance of WBS is better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures are designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes defects with spectral features, 2D-Unet emphasizes defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. Experimental results verified that the proposed HLDDA can effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading towards Industry 4.0. INDEX TERMSConstrained Energy Minimization (CEM), Deep Learning (DL), Hyperspectral image (HSI)

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“…As commented by Aslam et al [3], in a changing global scenario, the manufacturing industry constantly demands higher product quality and higher productivity to satisfy customer requirements and reduce rejection costs. The increased demands for objectivity, reliability and criminant Analysis (CLDA), Direct LDA (DLDA), Yang LDA (YLDA) and Kernel LDA (KLDA) are applied.…”

Section: Introductionmentioning

confidence: 99%

A decision support system for fault detection and definition of the quality of wet blue goat skins

Sousa¹,

Medeiros²,

Pereira³

et al. 2021

Heliyon

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The vast majority of goat skin processed by traditional tanneries comes from small rural producers. Thus, with the predominance of rustic creation, slaughter, and skinning methods, the batches of hides processed by tanneries have a very heterogeneous quality. Thus, there is a need to categorize the samples according to the quantity and location of defects. The categorization process is subjective and strongly influenced by the experience of the professional classifier, causing a lack of homogeneity in the composition of the goat hide lots for sale. Aiming to reduce failures in the categorization of goatskin samples, the authors investigate the application of computer vision and artificial intelligence on a set of previously categorized wet blue goatskin photographic samples. That said, is analyzed the capacity of different classifiers, with different paradigms, in detecting defects in goatskin samples and in categorizing these samples among seven possible quality levels. A hit rate of 95.9% was achieved in detecting defects and 93.3% in categorizing quality levels. The results suggest that the proposed methodology can be used as a decision aid tool in the qualification process of goat leather samples, which can reduce sample labeling errors.

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

Cited by 35 publications

References 66 publications

Automated Classification System for Tick-Bite Defect on Leather

Automated Classification System for Tick-Bite Defect on Leather

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

A decision support system for fault detection and definition of the quality of wet blue goat skins

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