The textile industry plays a vital role in the global economy, with constant advancements in technology enhancing production efficiency. One significant aspect of textile manufacturing involves identifying defects in machinery fabric, as these defects can compromise product quality and lead to substantial financial losses. This research focuses on developing advanced mathematical models to detect and classify defects in textile machinery fabric, using cutting-edge techniques from the fields of machine learning and computer vision.
| Mualliflar | Kayumov, Ahror |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2023-12-11 |
| Son | 4 |
| Betlar | 257-261 |
| Til | Rus |
Mathematical Models, Defect Identification, Textile Machinery Fabric, Machine Learning, Computer Vision, Convolutional Neural Networks, Image Segmentation, Object Recognition, Defect Detection, Fabric Quality Control, Mathematical Models, Defect Identification, Textile Machinery Fabric, Machine Learning, Computer Vision, Convolutional Neural Networks, Image Segmentation, Object Recognition, Defect Detection, Fabric Quality Control
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