{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T16:03:50Z","timestamp":1783958630315,"version":"3.55.0"},"reference-count":20,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51805386"],"award-info":[{"award-number":["51805386"]}]},{"name":"National Natural Science Foundation of China","award":["MADTOF2021B02"],"award-info":[{"award-number":["MADTOF2021B02"]}]},{"name":"Open Project of Metallurgical Automation and Testing Technology Engineering Research Center of the Ministry of Education","award":["51805386"],"award-info":[{"award-number":["51805386"]}]},{"name":"Open Project of Metallurgical Automation and Testing Technology Engineering Research Center of the Ministry of Education","award":["MADTOF2021B02"],"award-info":[{"award-number":["MADTOF2021B02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Timely and accurate flame detection is a very important and practical technology for preventing the occurrence of fire accidents effectively. However, the current methods of flame detection are still faced with many challenges in video surveillance scenarios due to issues such as varying flame shapes, imbalanced samples, and interference from flame-like objects. In this work, a real-time flame detection method based on deformable object detection and time sequence analysis is proposed to address these issues. Firstly, based on the existing single-stage object detection network YOLOv5s, the network structure is improved by introducing deformable convolution to enhance the feature extraction ability for irregularly shaped flames. Secondly, the loss function is improved by using Focal Loss as the classification loss function to solve the problems of the imbalance of positive (flames) and negative (background) samples, as well as the imbalance of easy and hard samples, and by using EIOU Loss as the regression loss function to solve the problems of a slow convergence speed and inaccurate regression position in network training. Finally, a time sequence analysis strategy is adopted to comprehensively analyze the flame detection results of the current frame and historical frames in the surveillance video, alleviating false alarms caused by flame shape changes, flame occlusion, and flame-like interference. The experimental results indicate that the average precision (AP) and the F-Measure index of flame detection using the proposed method reach 93.0% and 89.6%, respectively, both of which are superior to the compared methods, and the detection speed is 24\u201326 FPS, meeting the real-time requirements of video flame detection.<\/jats:p>","DOI":"10.3390\/s23208616","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T12:59:48Z","timestamp":1697893188000},"page":"8616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Real-Time Flame Detection Method Using Deformable Object Detection and Time Sequence Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Jingyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Sureland Industrial Fire Safety Limited, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Shi","sequence":"additional","affiliation":[{"name":"Sureland Industrial Fire Safety Limited, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wangming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"ref_1","first-page":"74","article-title":"Research on Multi-Sensor Method for Detecting Aircraft Cargo Fire","volume":"28","author":"He","year":"2018","journal-title":"China Saf. 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