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Liao Shujiang. A preliminary study on the trend of fire spread [J]. Fire Science and Technology, 2012, 31(7): 670–673.ShujiangLiaoA preliminary study on the trend of fire spread [J]. Fire Science and Technology, 2012, 31(7): 670–673.Search in Google Scholar
Wang M. Risk Information, Risk Perception and Fire Prevention Behavior [D]. University of Science and Technology of China, 2017.WangM.Risk Information, Risk Perception and Fire Prevention Behavior [D]. University of Science and Technology of China, 2017.Search in Google Scholar
Lv P T, Li J, Wu L Y et al. Research on automatic edge detection of fire video images [J]. Applied Science. 2003.LvP TLiJWuL YResearch on automatic edge detection of fire video images [J]. Applied Science. 2003.Search in Google Scholar
Yan Yunyang, Gao Shangbing, Guo Zhibo, et al. Automatic fire detection based on video images [J]. Computer Application Research, 2008, 25(4): 1075–1078. YunyangYanShangbingGaoZhiboGuoAutomatic fire detection based on video images [J]. Computer Application Research, 2008, 25(4): 1075–1078. Search in Google Scholar
Tan Yong, Xie Linbai, Feng Hongwei, et al. Imagebased flame detection algorithm [J]. Laser & Optoelectronics Progress, 2019, 56(16): 161012.YongTanLinbaiXieHongweiFengImagebased flame detection algorithm [J]. Laser & Optoelectronics Progress, 2019, 56(16): 161012.Search in Google Scholar
Cui Bingcheng, Cheng Naiwei, Zhao Peng. Exploration of smoke image detection method based on matlab [J]. Science and Technology Innovation. 2019, (28).BingchengCuiNaiweiChengPengZhaoExploration of smoke image detection method based on matlab [J]. Science and Technology Innovation. 2019,(28).Search in Google Scholar
Gong F, Li C, Gong W, et al. A real-time fire detection method from video with multifeature fusion [J]. Computational intelligence and neuroscience, 2019, 2019.GongFLiCGongWA real-time fire detection method from video with multifeature fusion [J]. Computational intelligence and neuroscience, 2019, 2019.Search in Google Scholar
Li P, Zhao W. Image fire detection algorithms based on convolutional neural networks [J]. Case Studies in Thermal Engineering, 2020, 19: 100625.LiPZhaoW.Image fire detection algorithms based on convolutional neural networks [J]. Case Studies in Thermal Engineering, 2020, 19: 100625.Search in Google Scholar
Saeed F, Paul A, Karthigaikumar P, et al. Convolutional neural network based early fire detection [J]. Multimedia Tools and Applications, 2020, 79: 9083–9099.SaeedFPaulAKarthigaikumarPConvolutional neural network based early fire detection [J]. Multimedia Tools and Applications, 2020, 79: 9083–9099.Search in Google Scholar
Zhang Chi, Meng Qinghao, Well Tao. Video flame detection algorithm based on improved GMM and multi-feature fusion [J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410006.ChiZhangQinghaoMengTaoWellVideo flame detection algorithm based on improved GMM and multi-feature fusion [J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410006.Search in Google Scholar
Jing K, Jia Y, Zhang C, et al. MobileAttentionNet: An Efficient Network for Semantic Segmentation of Forest Fire Images [C]//2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT). IEEE, 2021: 377–380.JingKJiaYZhangCMobileAttentionNet: An Efficient Network for Semantic Segmentation of Forest Fire Images [C]//2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT). IEEE, 2021: 377–380.Search in Google Scholar
Zhang L, Wang M, Ding Y, et al. MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection [J]. Forests, 2023, 14(3): 616.ZhangLWangMDingYMS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection [J]. Forests, 2023, 14(3): 616.Search in Google Scholar
Zhang X, Qian K, Jing K, et al. Fire detection based on convolutional neural networks with channel attention [C]//2020 Chinese Automation Congress (CAC). IEEE, 2020: 3080–3085.ZhangXQianKJingKFire detection based on convolutional neural networks with channel attention [C]//2020 Chinese Automation Congress (CAC). IEEE, 2020: 3080–3085.Search in Google Scholar
Zhao Yuanyuan, Zhu Jun, Xie Yakun, et al. Improved Yolo-v3 algorithm for real-time flame detection in video images [J]. Journal of Wuhan University (Information Science Edition), 2021, 46(3): 326–334.YuanyuanZhaoJunZhuYakunXieImproved Yolo-v3 algorithm for real-time flame detection in video images [J]. Journal of Wuhan University (Information Science Edition), 2021, 46(3): 326–334.Search in Google Scholar
Ding Hao, Wang Huiqin, Wang Ke. Improved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement [J]. Laser & Optoelectronics Progress, 2022, 59(24):2410003-2410003-9.HaoDingHuiqinWangKeWangImproved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement [J]. Laser & Optoelectronics Progress, 2022, 59(24):2410003-2410003-9.Search in Google Scholar
Avazov K, Mukhiddinov M, Makhmudov F, et al. Fire detection method in smart city environments using a deep-learning-based approach [J]. Electronics, 2021, 11(1): 73.AvazovKMukhiddinovMMakhmudovFFire detection method in smart city environments using a deep-learning-based approach [J]. Electronics, 2021, 11(1): 73.Search in Google Scholar
Sun J, Ge H, Zhang Z. AS-YOLO: An improved YOLOv4 based on attention mechanism and SqueezeNet for person detection [C]//2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021, 5: 1451–1456.SunJGeHZhangZ.AS-YOLO: An improved YOLOv4 based on attention mechanism and SqueezeNet for person detection [C]//2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021, 5: 1451–1456.Search in Google Scholar
Xue Q, Lin H, Wang F. Fcdm: an improved forest fire classification and detection model based on yolov5 [J]. Forests, 2022, 13(12): 2129.XueQLinHWangF.Fcdm: an improved forest fire classification and detection model based on yolov5 [J]. Forests, 2022, 13(12): 2129.Search in Google Scholar
Xue Z, Lin H, Wang F. A small target forest fire detection model based on YOLOv5 improvement[J]. Forests, 2022, 13(8): 1332.XueZLinHWangF.A small target forest fire detection model based on YOLOv5 improvement[J]. Forests, 2022, 13(8): 1332.Search in Google Scholar
Yang T, Xu S, Li W, et al. A smoke and flame detection method using an improved yolov5 algorithm [C]//2022 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2022: 366371.YangTXuSLiWA smoke and flame detection method using an improved yolov5 algorithm [C]//2022 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2022: 366371.Search in Google Scholar
Yang X, Wang Z, He Y, et al. Research on open flame recognition algorithm in construction site based on attention mechanism [C]//2023 15th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2023: 1–6.YangXWangZHeYResearch on open flame recognition algorithm in construction site based on attention mechanism [C]//2023 15th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2023: 1–6.Search in Google Scholar
Zhang H, Wang Y, Dayoub F, et al. VariFocalnet: An iou-aware dense object detector [C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8514–8523.ZhangHWangYDayoubFVariFocalnet: An iou-aware dense object detector [C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8514–8523.Search in Google Scholar
Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module [C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3–19.WooSParkJLeeJ YCbam: Convolutional block attention module [C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3–19.Search in Google Scholar