Issue |
Wuhan Univ. J. Nat. Sci.
Volume 27, Number 6, December 2022
|
|
---|---|---|
Page(s) | 539 - 549 | |
DOI | https://doi.org/10.1051/wujns/2022276539 | |
Published online | 10 January 2023 |
CLC number: TP 399
Semantic Segmentation Using DeepLabv3+ Model for Fabric Defect Detection
1
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2
School of Textile and Fashion Technology, Shanghai University of Engineering Science, Shanghai 201620, China
† To whom correspondence should be addressed. E-mail: xinbj@sues.edu.cn
Received:
5
September
2022
Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of corresponding features to complete the detection. To overcome the limitations of traditional algorithms, deep learning-based correlative algorithms can extract more complex image features and perform better in image classification and object detection. A pixel-level defect segmentation methodology using DeepLabv3+, a classical semantic segmentation network, is proposed in this paper. Based on ResNet-18, ResNet-50 and Mobilenetv2, three DeepLabv3+ networks are constructed, which are trained and tested from data sets produced by capturing or publicizing images. The experimental results show that the performance of three DeepLabv3+ networks is close to one another on the four indicators proposed (Precision, Recall, F1-score and Accuracy), proving them to achieve defect detection and semantic segmentation, which provide new ideas and technical support for fabric defect detection.
Key words: fabric defect detection / semantic segmentation / deep learning / DeepLabv3+
Biography: ZHU Runhu, male, Master candidate, research direction: fabric defect detection. E-mail: zhurunhufj@163.com
Supported by the National Natural Science Foundation of China (61876106) and Shanghai Local Capacity-Building Project (19030501200)
© Wuhan University 2022
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