{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T23:38:25Z","timestamp":1785800305474,"version":"3.56.0"},"reference-count":79,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Technology and Standards","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Korea Agency for Technology and Standards","award":["K_G012002236201"],"award-info":[{"award-number":["K_G012002236201"]}]},{"name":"Korea Agency for Technology and Standards","award":["202208820001"],"award-info":[{"award-number":["202208820001"]}]},{"name":"Gachon University","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Gachon University","award":["K_G012002236201"],"award-info":[{"award-number":["K_G012002236201"]}]},{"name":"Gachon University","award":["202208820001"],"award-info":[{"award-number":["202208820001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model\u2019s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model\u2019s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model\u2019s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model\u2019s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.<\/jats:p>","DOI":"10.3390\/s23208374","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T10:23:42Z","timestamp":1696933422000},"page":"8374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":111,"title":["An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images"],"prefix":"10.3390","volume":"23","author":[{"given":"Saydirasulov Norkobil","family":"Saydirasulovich","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1424-0799","authenticated-orcid":false,"given":"Mukhriddin","family":"Mukhiddinov","sequence":"additional","affiliation":[{"name":"Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent 100208, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0478-7889","authenticated-orcid":false,"given":"Oybek","family":"Djuraev","sequence":"additional","affiliation":[{"name":"Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent 100208, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea"},{"name":"Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"ref_1","unstructured":"Hoover, K., and Hanson, L.A. 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