{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T01:07:40Z","timestamp":1710983260886},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"earmarked fund for CARS-50"},{"name":"Central Public-interest Scientific Institution Basal Research Fund CAFS","award":["2023TD86"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Kelp, often referred to as a \u201csea vegetable\u201d, holds substantial economic significance. Currently, the drying process for kelp in China primarily relies on outdoor sun-drying methods. Detecting kelp in the field presents challenges arising from issues such as overlapping and obstruction. To address these challenges, this study introduces a lightweight model, K-YOLOv5, specifically designed for the precise detection of sun-dried kelp. YOLOv5-n serves as the base model, with several enhancements implemented in this study: the addition of a detection head incorporating an upsampling layer and a convolution module to improve the recognition of small objects; the integration of an enhanced I-CBAM attention mechanism, focusing on key features to enhance the detection accuracy; the replacement of the CBS module in the neck network with GSConv to reduce the computational burden and accelerate the inference speed; and the optimization of the IoU algorithm to improve the identification of overlapping kelp. Utilizing drone-captured images of sun-dried kelp, a dataset comprising 2190 images is curated. Validation on this self-constructed dataset indicates that the improved K-YOLOv5 model significantly enhances the detection accuracy, achieving 88% precision and 78.4% recall. These values represent 6.8% and 8.6% improvements over the original model, respectively, meeting the requirements for the real-time recognition of sun-dried kelp.<\/jats:p>","DOI":"10.3390\/s24061971","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T09:04:12Z","timestamp":1710925452000},"page":"1971","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Sun-Dried Kelp Detection: Introducing K-YOLO, a Lightweight Model with Improved Precision and Recall"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0009-0006-3824-0394","authenticated-orcid":false,"given":"Zhefei","family":"Xiao","sequence":"first","affiliation":[{"name":"Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2897-8062","authenticated-orcid":false,"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[{"name":"Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China"}]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[{"name":"Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China"}]},{"given":"Tiantian","family":"Ma","sequence":"additional","affiliation":[{"name":"Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/23308249.2018.1509056","article-title":"Remote Sensing of Kelp (Laminariales, Ochrophyta): Monitoring Tools and Implications for Wild Harvesting","volume":"27","author":"Bennion","year":"2019","journal-title":"Rev. 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