Automated Classification System for Tick-Bite Defect on Leather

Abstract

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.

Comparative study on deep-learning-based leather surface defect identification

“…Owing to the scarcity of data, based on Liong's above work [31], Gan et al [34] adopted a generative adversarial network as a means of reliably synthesizing further normal images in order to augment an already limited training set, and therefore improve the accuracy of feature extraction and classification of the typical AlexNet. Another study [35] of Liong's team is to extract features using AlexNet, and use support vector machine (SVM) as a classifier to identify obvious opening defects. The dataset contained 560 leather images with a resolution of 140 × 140 pixels.…”