Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation

Journal of Leather Science and Engineering

卷:4

Denis Ashok Sathia Seelan¹  Praveen Kumar Moganam¹ 

[1] Department of Design Automation, Cyber Physical Systems Laboratory, School of Mechanical Engineering, Vellore Institute of Technology;

关键词: Convolution neural networks;    Machine learning classifier;    Leather defects;    Multi class classification;    Class activation map;    Segmentation;   

DOI  :  10.1186/s42825-022-00080-9

来源: DOAJ

【 摘 要 】

Abstract Modern leather industries are focused on producing high quality leather products for sustaining the market competitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature; hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is necessary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classification of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented. Graphical Abstract

【 授权许可】

Unknown