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Finally, five more common types of defects were selected for training in the GuangDong TianChi fabric defect dataset, and using our proposed PEI-YOLOv5 with only 0.2 Giga Floating Point Operations (GFLOPs) increase, the mAP improved by 3.61%, reaching 87.89%. To demonstrate the versatility of PEI-YOLOv5, we additionally evaluated this in the NEU surface defect database, with the mAP of 79.37%. The performance of PEI-YOLOv 5 in these two datasets surpasses the most advanced fabric defect detection methods at present. We deployed the model to the NVIDIA Jetson TX2 embedded development board, and the detection speed reached 31 frames per second (Fps), which can fully meet the speed requirements of real-time detection.<\/jats:p>","DOI":"10.1007\/s40747-023-01317-8","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T03:02:58Z","timestamp":1706842978000},"page":"3371-3387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A real-time and accurate convolutional neural network for fabric defect detection"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3027-0460","authenticated-orcid":false,"given":"Xueshen","family":"Li","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"1317_CR1","doi-asserted-by":"publisher","unstructured":"Bullon J, Gonz\u00b4alez Arrieta A, Hern\u00b4andez Encinas A et al. 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