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Baobao Liu, Heying Wang, Zifan Cao, Yu Wang, Lu Tao, Jingjing Yang and Kaibing Zhang
Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a ...
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Long Li, Qi Li, Zhiyuan Liu and Lin Xue
The research results can quickly and accurately detect defects in the fabric production process.
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Yavuz Kahraman and Alptekin Durmusoglu
Fabric quality has an important role in the textile sector. Fabric defect, which is a highly important factor that influences the fabric quality, has become a concept that researchers are trying to minimize. Due to the limited capacity of human resources...
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Xi Yue, Qing Wang, Lei He, Yuxia Li and Dan Tang
Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant p...
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Peiran Peng, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu and Weihu Zhou
Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Co...
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