{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:45:52Z","timestamp":1775043952032,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12202428"],"award-info":[{"award-number":["12202428"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Mosquitoes of the genera Aedes and Culex are major vectors of mosquito-borne diseases, posing serious threats to public health. Accurate detection of these species is therefore crucial for disease prevention and vector control. Traditional identification methods are time-consuming, labor-intensive, and prone to human error. With the rapid development of deep learning, automated mosquito detection has become feasible; however, existing object detection models still struggle with small-object recognition and high computational complexity. To address these limitations, this study constructs a self-developed dataset and proposes a lightweight mosquito detection model based on YOLOv8, termed LW-YOLO. The model integrates HGNetv2, Rep-Ghost, and SCDH modules into the backbone, neck, and head, respectively, enhancing both detection accuracy and computational efficiency. Experimental results show that LW-YOLO achieves a precision of 0.978, recall of 0.972, and mAP50 of 0.987, improving by 1.6%, 1.25%, and 0.7% over the baseline YOLOv8. Meanwhile, its parameter count and computational cost are reduced from 3.0 M and 8.1 GFLOPs to 1.2 M and 4.4 GFLOPs, corresponding to decreases of 60% and 45.7%, respectively. The proposed LW-YOLO model not only achieves accurate detection of Aedes and Culex mosquitoes, providing technical support for mosquito-borne disease prevention, but also offers a promising lightweight solution for deployment on resource-constrained embedded or edge devices.<\/jats:p>","DOI":"10.3390\/a19040267","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:09:21Z","timestamp":1775038161000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LW-YOLO: A Lightweight Enhanced YOLOv8-Based Model for Mosquito Detection"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9494-7704","authenticated-orcid":false,"given":"Jiahao","family":"Duan","sequence":"first","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-4816","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deming","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4437-2212","authenticated-orcid":false,"given":"Ming","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiling","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3424-8325","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Infectious Disease Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Center), Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"ref_1","first-page":"74","article-title":"Study on Medical Vectors and Infectious Disease Spreading","volume":"29","author":"Li","year":"2006","journal-title":"Chin. 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