{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:55:40Z","timestamp":1790567740607,"version":"4.1.0"},"reference-count":42,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T00:00:00Z","timestamp":1789257600000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ift.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Food Science"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>In response to the challenges of insufficient detection accuracy, limited processing speed, and poor recognition of long\u2010tail categories in intelligent catering scenarios, this paper proposes YOLO\u2010MCD (YOLO\u2010multi\u2010Chinese\u2010dish), a fast and accurate multi\u2010dish detection method. Based on YOLO11, YOLO\u2010MCD introduces three key improvements: the design of the C3K2_RFCBAMConv module to enhance feature extraction capability; the incorporation of an EUCB upsampling structure to strengthen multi\u2010scale feature fusion; and the construction of a dynamic loss function EMASlideLoss, which integrates the exponential moving average with an IoU\u2010based adaptive weighting mechanism to significantly improve the detection performance of long\u2010tail categories. Experiments on a self\u2010built multi\u2010dish dataset demonstrate that YOLO\u2010MCD achieves 91.2% mAP@50 and 86.4% mAP@50\u201395, with an inference speed of 278 FPS, outperforming YOLO11n by 2.2% in mAP@50 while increasing the parameter count by only 7%. Compared with SSD, Faster R\u2010CNN, and RT\u2010DETR\u2010R18, YOLO\u2010MCD exhibits superior performance in both accuracy and speed. Furthermore, ablation studies verify the independent effectiveness and synergistic gains of each module. YOLO\u2010MCD maintains real\u2010time inference capability while achieving high\u2010precision detection for long\u2010tail distributions, multi\u2010object scenes, and complex backgrounds, demonstrating great potential for applications in intelligent catering and food safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.71484","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T03:52:43Z","timestamp":1789357963000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO\u2010MCD: An Efficient Detection Method for Multi\u2010Dish Recognition"],"prefix":"10.1111","volume":"91","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5291-5706","authenticated-orcid":false,"given":"Shoujie","family":"Yu","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science Zhejiang A&amp;F University, Lin'an  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0100-1780","authenticated-orcid":false,"given":"Yinhui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science Zhejiang A&amp;F University, Lin'an  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Gu","sequence":"additional","affiliation":[{"name":"Tianmu Mountain Forest Farm, Lin'an  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshen","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science Zhejiang A&amp;F University, Lin'an  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianxin","family":"Xu","sequence":"additional","affiliation":[{"name":"Anxiao Technology (Hangzhou) Co. 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