{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:06:55Z","timestamp":1735016815414,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>The rapid separation of tobacco shreds and stems is a critical stage in the cigarette production process. Compared to traditional mechanical separation devices, machine vision technology combined with deep learning models offers advantages in accuracy, adaptability, and flexibility for automated air separation equipment. However, intelligent separation mechanisms based on machine vision also face operational challenges, including imbalanced training sample distribution, motion artifacts in targets, difficulty in sample annotation, and the small size of tobacco shreds and stems, which makes feature extraction challenging. This study first collected actual images from the outlet of the ZJ17 cigarette making machine, constructing a dataset consisting of 105 annotated static images of tobacco shreds and stems, and 50 dynamic images, divided into training and testing sets in an 8:2 ratio. Next, this study used the YOLOv8 model to train and predict the tobacco shreds and stems dataset, achieving classification and detection of tobacco shreds and stems. Finally, the model\u2019s performance was evaluated using mAP50, Precision, Recall, and Confidence metrics, achieving an overall mAP50 of 0.796, Precision of 0.81, and Recall of 0.8068. Specifically, the Precision was 0.855 for stems and 0.764 for shreds, with Recalls of 0.764 and 0.647, respectively. Visualization of the detection results showed high consistency between the model\u2019s predictions and the original annotations, demonstrating its reliability and accuracy in practical applications. This study successfully solved the challenge of accurate small target multi-scale feature extraction and demonstrated the effectiveness of the model in real-world scenarios.<\/jats:p>","DOI":"10.3233\/faia241444","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:01Z","timestamp":1734947341000},"source":"Crossref","is-referenced-by-count":0,"title":["Application of the YOLOv8x Model in Visual Stem Detection During Cigarette Production"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0771-3457","authenticated-orcid":false,"given":"Jiangtao","family":"Zhang","sequence":"first","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]},{"given":"Baohua","family":"Li","sequence":"additional","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]},{"given":"Ying","family":"Qin","sequence":"additional","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]},{"given":"Dan","family":"Yan","sequence":"additional","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]},{"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]},{"given":"You","family":"Liu","sequence":"additional","affiliation":[{"name":"Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd, Baoding, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241444","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:01Z","timestamp":1734947341000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241444","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}