Intermittent Leather Defect Detection Based on Ensemble Algorithms Derived from Black Hat Transformation and Hough Transformation

Vasagam, Swamiraj Nithiyanantha; Sornam, M.

doi:10.1007/978-981-16-5655-2_4

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Vasagam, S.N. and Sornam, M. 27 • Pixel intensity has been extracted on five regions based on Horizontal and Vertical mapping of coordinates.…”

Section: Introductionmentioning

confidence: 99%

Region Wise Surface Level Defect Detection and Ranking of Crust Leather Images Based on Image Processing Techniques

Vasagam,

Sornam

2023

JALCA

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Sorting and aligning of crust leather for grading on position wise defect distribution is one of the methods adopted in the tanning industry. This method is generally carried out manually by a veteran on official sampling position and their input is critical because it is directly linked to sales of the crust leather. The opinion of the experts is believed to be stable and consumes a good amount of time too. Hence, in the current research a robust defect detection method and ranking of crust leather images based on image processing techniques is proposed to give a stable solution in a short span of time. A custom-made dataset of crust leather images consisting of 5640 images were used in this study. The pixel intensity has been extracted on demarcated position of various regions including neck, belly left, belly right, center and butt instead of official sampling position through horizontal and vertical mapping of coordinates with a new method Grading Score on Image Position wise (GSIP) on the actual images. The image processing techniques using Canny Edge Detection and filters such as Laplacian, Median, Prewitt, Roberts, Sobel and Scharr were implemented to get the pixel intensity grouped and classified based on parameters within acceptable range using a Naïve Bayes Classifier. The classifier confirms that the accuracy of Set I - Actual Images and Set II - Defects with implementation of canny edge detection over other image processing techniques at 99.50%. Therefore, the current research confirms that the proposed GSIP method would give an additional tool to inspectors while ranking the crust leather based on region wise surface level defect detection of crust leather images based on image processing techniques.

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“…Vasagam, S.N. and Sornam, M. 27 • Pixel intensity has been extracted on five regions based on Horizontal and Vertical mapping of coordinates.…”

Section: Introductionmentioning

confidence: 99%

Region Wise Surface Level Defect Detection and Ranking of Crust Leather Images Based on Image Processing Techniques

Vasagam,

Sornam

2023

JALCA

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A Systematic Review of Machine-Vision-Based Leather Surface Defect Inspection

et al. 2022

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Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.

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Intermittent Leather Defect Detection Based on Ensemble Algorithms Derived from Black Hat Transformation and Hough Transformation

Cited by 2 publications

References 15 publications

Region Wise Surface Level Defect Detection and Ranking of Crust Leather Images Based on Image Processing Techniques

Region Wise Surface Level Defect Detection and Ranking of Crust Leather Images Based on Image Processing Techniques

A Systematic Review of Machine-Vision-Based Leather Surface Defect Inspection

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