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Detection of Fiber Defects Using Keypoints and Deep Learning

Siegmund, Dirk and Fu, Biying and José-García, Adán and Salahuddin, Ahmad and Kuijper, Arjan (2020):
Detection of Fiber Defects Using Keypoints and Deep Learning.
In: International Journal of Pattern Recognition and Artificial Intelligence, ISSN 0218-0014,
DOI: 10.1142/S0218001421500166,
[Article]

Abstract

Due to the deforming and dynamically changing textile fibers, the quality assurance of cleaned industrial textiles is still a mostly manual task. Usually, textiles need to be spread flat, in order to detect defects using computer vision inspection methods. Already known methods for detecting defects on such inhomogeneous, voluminous surfaces use mainly supervised methods based on deep neural networks and require lots of labeled training data. In contrast, we present a novel unsupervised method, based on SURF keypoints, that does not require any training data. We propose using their location, number and orientation in order to group them into geographically close clusters. Keypoint clusters also indicate the exact position of the defect at the same time. We furthermore compared our approach to supervised methods using deep learning. The presented processing pipeline shows how normalization and classification methods need to be combined, in order to reliably detect fiber defects such as cuts and holes. We evaluate the performance of our system in real-world settings with images of piles of textiles, taken in stereo vision. Our results show that our novel unsupervised classification method using keypoint clustering achieves comparable results to other supervised methods.

Item Type: Article
Erschienen: 2020
Creators: Siegmund, Dirk and Fu, Biying and José-García, Adán and Salahuddin, Ahmad and Kuijper, Arjan
Title: Detection of Fiber Defects Using Keypoints and Deep Learning
Language: English
Abstract:

Due to the deforming and dynamically changing textile fibers, the quality assurance of cleaned industrial textiles is still a mostly manual task. Usually, textiles need to be spread flat, in order to detect defects using computer vision inspection methods. Already known methods for detecting defects on such inhomogeneous, voluminous surfaces use mainly supervised methods based on deep neural networks and require lots of labeled training data. In contrast, we present a novel unsupervised method, based on SURF keypoints, that does not require any training data. We propose using their location, number and orientation in order to group them into geographically close clusters. Keypoint clusters also indicate the exact position of the defect at the same time. We furthermore compared our approach to supervised methods using deep learning. The presented processing pipeline shows how normalization and classification methods need to be combined, in order to reliably detect fiber defects such as cuts and holes. We evaluate the performance of our system in real-world settings with images of piles of textiles, taken in stereo vision. Our results show that our novel unsupervised classification method using keypoint clustering achieves comparable results to other supervised methods.

Journal or Publication Title: International Journal of Pattern Recognition and Artificial Intelligence
Uncontrolled Keywords: Computer vision, Deep learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 25 Jan 2021 11:32
DOI: 10.1142/S0218001421500166
Additional Information:

Art.No.: 2150016, Online Ready

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