S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2796

Integrated Image Sensor and Deep Learning Network for Fabric Pilling Classification

Chi-Huang Shih, Cheng-Jian Lin, and Chin-Ling Lee

(Received April 15, 2021; Accepted November 12, 2021)

Keywords: deep learning network, fabric image, pilling level classification, image sensor

Manufacturers’ fabrics are tested for abrasion resistance before leaving the factory, and the fabrics are manually visually graded to ensure that there are no defects. However, manual visual classification consumes a lot of human resources. In addition, long-term visual inspections using the eyes often result in occupational injuries. As a result, the overall efficiency is reduced. To overcome and avoid such situations, we devised an image preprocessing technology and deep learning network for classifying the pilling level of knitted fabrics. In the first step, fabric images are collected using an image optical sensor. The fast Fourier transform (FFT) and Gaussian filter are used for image preprocessing to strengthen the pilling characteristics in the fabric images. In the second step, the characteristics and classification of fabric pilling are automatically captured and identified using a deep learning network. The experimental results show that the average accuracy of the proposed method for pilling level classification is 100%. The proposed method has 0.3% and 2.7% higher average accuracy than deep-principal-component-analysis-based neural networks (DPCANN) and the type-2 fuzzy cerebellar model articulation controller (T2FCMAC), respectively, demonstrating the superiority of the proposed model.

Corresponding author: Cheng-Jian Lin




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