{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:44:27Z","timestamp":1777574667746,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discriminative with respect to dynamic fire and have lower detection precision when a fire changes. Therefore, we propose a dynamic convolution YOLOv5 fire detection method using a video sequence. Our method first uses the K-mean++ algorithm to optimize anchor box clustering; this significantly reduces the rate of classification error. Then, the dynamic convolution is introduced into the convolution layer of YOLOv5. Finally, pruning of the network heads of YOLOv5\u2019s neck and head is carried out to improve the detection speed. Experimental results verify that the proposed dynamic convolution YOLOv5 fire detection method demonstrates better performance than the YOLOv5 method in recall, precision and F1-score. In particular, compared with three other deep learning methods, the precision of the proposed algorithm is improved by 13.7%, 10.8% and 6.1%, respectively, while the F1-score is improved by 15.8%, 12% and 3.8%, respectively. The method described in this paper is applicable not only to short-range indoor fire identification but also to long-range outdoor fire detection.<\/jats:p>","DOI":"10.3390\/s22082929","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T22:48:45Z","timestamp":1649803725000},"page":"2929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing"],"prefix":"10.3390","volume":"22","author":[{"given":"Qing","family":"An","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-2854","authenticated-orcid":false,"given":"Xijiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430079, China"}]},{"given":"Junqian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430079, China"}]},{"given":"Ruizhe","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430079, China"}]},{"given":"Yuanjun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"ARHPE: Asymmetric Relation-Aware Representation Learning for Head Pose Estimation in Industrial Human-Computer Interaction","volume":"32","author":"Liu","year":"2022","journal-title":"IEEE Trans. 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