Vision-based carpet similarity inspection using deep learning and genetic algorithms
Department
Robotics and Mechatronics Engineering
Document Type
Article
Publication Date
4-29-2021
Abstract
An automatic carpet similarity inspection system based on computer vision and machine learning is developed in this article to replace the traditional inspection approach using human eyes in the carpet industry. First, each carpet image is extracted a feature vector using a popular deep learning model (Inception-V3). Then the unsupervised clustering learning algorithm is employed to automatically assign the carpets to different groups based on their similarity. Finally, a genetic algorithm-based approach is proposed to find the optimal arrangement of the carpets in each group, as required by the industrial carpet plant. The experimental results validate that the proposed approach is successful and effective.
Journal Title
Mechatronic Systems and Control
Journal ISSN
25611771
Volume
49
Issue
3
First Page
157
Last Page
163
Digital Object Identifier (DOI)
10.2316/J.2021.201-0239