[1]ZHOU Zaiyong,DI Lan.Research on textile defect detection method combining attention mechanism[J].CAAI Transactions on Intelligent Systems,2024,19(4):827-838.[doi:10.11992/tis.202304045]
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Research on textile defect detection method combining attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 827-838 Column: 学术论文—机器学习 Public date: 2024-07-05
Title:
Research on textile defect detection method combining attention mechanism
Author(s):
ZHOU Zaiyong1;  DI Lan2
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China;
2. School of Digital Media, Jiangnan University, Wuxi 214122, China
Keywords:
attention mechanism; scale aggregation; bilinear interpolation; discrete cosine transform; multi-scale feature; feature fusion; textile defect detection; computer vision
CLC:
TP391
DOI:
10.11992/tis.202304045
Abstract:
This paper presents a textile defect detection model called SAAM-YOLOX, which is based on the improved YOLOX. The model aims to address the issues of false positives and false negatives in textile defect detection, particularly in detecting Houndstooth and Gingham backgrounds, as well as the problem of overall low detection accuracy. In the feature extraction stage, the model introduces a multi-branch discrete cosine attention mechanism (MDCA) based on the discrete cosine transform to address false positives and false negatives in the Houndstooth and Gingham backgrounds, and thereby improve the detection accuracy. In the feature fusion stage, the SAAM-YOLOX model adopts scale aggregation technology and attention mechanism to construct a scale aggregation attention module (SAAM) to aggregate and enhance semantic features of different scales. SAAM uses bilinear interpolation combined with self-attention mechanism to enhance validity of feature information in the upsampling process, further improving the detection accuracy. After completing the scale aggregation, an attention module is added to enhance the mixed-scale feature representation, ultimately achieving the goal of improving detection performance. Experimental results demonstrate that the proposed detection model can effectively solve the problem of false positives and false negatives in Houndstooth and Gingham backgrounds, and improve the accuracy of defect detection.
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