• [1]

    CHEN J , HE Y , WANG J . Multi-feature fusion based fast video flame detection[J]. Building & Environment, 2010, 45(5): 1113-1122.

  • [2]

    MUELLER M , KARASEV P , KOLESOV I , et al . Optical flow estimation for flame detection in videos[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2786-2797.

  • [3]

    HONG W B , PENG J W , CHEN C Y . A new image-based real-time flame detection method using color analysis[C]// IEEE International Conference on Networking, Sensing and Control, 2005. Tucson, AZ, USA: IEEE, 2005: 100-105.

  • [4]

    曾思通,吴海彬,沈培辉 . 基于多特征融合的视频火焰检测方法研究[J].图学学报,2017,8(4): 549-557.

  • [5]

    李新利, 李楠, 孙愉佳, 等 . 火焰自由基成像和极限学习机在NOx排放预测中的研究[J]. 系统仿真学报, 2016, 28(5):186-192.

  • [6]

    杨俊,王润生 . 基于计算机视觉的视频火焰检测技术[J].中国图像象图形学报,2008,13(7): 1223-1233.

  • [7]

    吴茜茵,严云洋,杜静, 等 .视频火焰检测综述[J].计算机科学与应用,2013, 3: 336-343.

  • [8]

    何立夫, 陆佳政, 刘毓, 等 . 输电线路山火可见光-红外多光源精准定位技术[J]. 高电压技术, 2018, 44(8): 136-142.

  • [9]

    张叶 . 森林火情烟雾识别算法研究[J]. 仪器仪表学报, 2014, 35(6): 103-106.

  • [10]

    林宏, 刘志刚, 赵同林, 等 . 基于视频的林火烟雾识别算法研究[J].安全与环境学报, 2013, 13(2): 210-214.

  • [11]

    赖孝君, 阎璐, 冯宪周,等 . 基于红外视频的火灾探测算法[J]. 导航与控制, 2013, 12(4): 30-36.

  • [12]

    WANG W . An experimental investigation of a premixed laminar flame using tunable diode laser and quantitative imaging of radiation intensity[J]. Dissertations & Theses-Gradworks, 2015: 17-19.

  • [13]

    刘丽媗, 庄坤森 . 基于热释电传感器的红外火焰探测系统的研究[J].宁德师范学院学报:自然科学版, 2013, 25(3): 287-292.

  • [14]

    YUAN Chi , ZHANG Youmin , LIU Zhixiang . A survey on technologies for automatic forest monitoring, detection, and fighting using unmanned aerial vechicles and remote sensing techniques[J].Research Press, 2015(45): 783-792.

  • [15]

    ZHAN Qian , SUN Fengdong , LI Wenhui . Flame detection using generic color model and improved block-based PCA in active infrared camera[J].International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(5): 1-15.

  • [16]

    张慧珍, 严云洋, 刘以安, 等 .基于超像素分割与闪频特征判别的视频火焰检测[J].数据采集与处理, 2018, 33(3): 512-520.

  • [17]

    CHINO D Y T , AVALHAIS L P S , RODRIGUES J F , et al . BoWFire: Detection of fire in still images by integrating pixel color and texture analysis[C]// Proceedings of 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. Salvador, Brazil: IEEE, 2015: 15573138.

  • [18]

    王媛彬, 任杰英 . 基于颜色特征的低照度林火图像分割方法[J]. 消防科学与技术, 2017, 36(10): 1401-1404.

  • [19]

    MATLANI P , SHRIVASTAVA M . A survey on video smoke detection[J]. Information and Communication Technology for Sustainable Development, 2018, 8(9): 221-222.

  • [20]

    LIU Y F , LIU Z G , WANG Y F , et al . Fast algorithm for YCbCr to HSV conversion based on fix-point DSP[J]. Application Research of Computers, 2012, 29(2): 741-738.

  • [21]

    LIU Yan , WU Wei , WU Zhaohui ,et al . Fire Detection in radiant energy domain for video surveillance[C]// Proceedings of 2015 International Conference on Virtual Reality and Visualization (ICVRV). [S.l.]: IEEE Computer Society, 2015: 1-8.

  • [22]

    邵良杉, 郭雅婵 . 基于Codebook的视频火焰识别算法[J]. 计算机应用, 2015, 35(5):1483-1487.

  • [23]

    JIANG Xiangang , HU Chuanxiu , FAN Zizhu . Research on flame detection method by fusion feature and sparse representation classification[J].International Journal of Computer and Communication Engineering,2016,5(4): 238-245.

  • [24]

    APPANA D K , ISLAM R , KHAN S A , et al . A video-based detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems[J].Information Sciences, 2017,8: 91-101.

  • [25]

    赵亮,骆炎民,骆翔宇 . 基于背景动态更新与暗通道先验的火灾烟雾检测算法[J].计算机应用更新,2017, 34(3): 957-960.

  • [26]

    陈俊周, 李炜, 王春瑶 . 一种动态场景下的视频目分割方法[J]. 电子科技大学学报, 2014, 43(2): 252-255.

  • [27]

    KAABI R , FRIZZI S , BOUCHOUICHA M . Video smoke detection review[C]//Proceedings of International Conference on Smart, Monitored and Controlled Cities(SM 2

  • [28]

    LI Zhenglin , ISUPOVA O , MIHAYLOVA L , et al . Autonomous flame detection in video based on saliency analysis and optical flow[C]// Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. [S.l.]: IEEE, 2017: 218-223.

  • [29]

    吴垠, 李良福, 肖樟树, 等 . 基于尺度不变特征的光流法目标跟踪技术研究[J]. 计算机工程与应用, 2013, 49(15): 157-161.

  • [30]

    陈磊, 黄继风 . 基于视频的火焰检测方法[J]. 计算机工程与设计, 2014, 35(9): 3143-3147.

  • [31]

    王文豪, 陈晓兵, 刘金岭 . 基于连通区域和SVM特征融合的火灾检测[J]. 计算机仿真, 2014, 31(1):383-387.

  • [32]

    WANG Lin , LI Aiguo . Early fire recognition based on multi-feature fusion of video smoke[C]//Proceedings of 36th Chinese Control Conference. Dalian, China: [s.n.], 2017: 5318-5323.

  • [33]

    吴亮生, 雷欢, 黄东运 . 基于混合高斯运动检测模型与多特征的烟雾识别算法[J]. 自动化与信息工程, 2014(2): 1-5.

  • [34]

    梅建军, 张为 . 基于ViBe与机器学习的早期火灾检测算法[J]. 光学学报, 2018, 38(7): 52-59.

  • [35]

    严云洋, 吴茜茵, 杜静, 等 . 基于色彩和闪频特征的视频火焰检测[J]. 计算机科学与探索, 2014, 8(10):1271-1279.

  • [36]

    卢鑫, 曹江涛, 姬晓飞, 等 . 基于多特征的火灾监控系统设计[J]. 辽宁石油化工大学学报, 2018, 39(1): 92-98.

  • [37]

    杨亚洁, 薛静, 乔鸿海, 等 . 基于多特征匹配的视频图像火灾火焰检测方法研究[J]. 电子设计工程, 2014, 22(3): 186-189.

  • [38]

    宋宁, 强彦, 董林佳 . 林火监测中基于视觉的火焰检测方法[J]. 科学技术与工程, 2017(25): 268-273.

  • [39]

    SEEBAMRUNGSAT J , PRAISING S , RIYAMONGKOL P . Fire detection in the buildings using image processing[C]//Proceedings of Student Project Conference. [S.l.]: IEEE, 2014: 95-98.

  • [40]

    CETIN A E , DIMITROPOULOS K , GOUVERNEUR B , et al . Video fire detection—Review[J]. Digital Signal Processing, 2013, 23(7): 1827-1843.

  • [41]

    耿庆田, 于繁华, 赵宏伟,等 . 基于颜色特征的火焰检测新算法[J]. 吉林大学学报: 工学版, 2014, 44(6): 1787-1792.

  • [42]

    何大超,娄小平,唐辉 .基于动态特征的实时烟雾检测[J].计算机应用与软件,2014,31(2): 202-204.

  • [43]

    聂建豪, 李士进 . 基于图像识别的秸秆焚烧事件检测[J]. 计算机技术与发展, 2017(5): 69-72.

  • [44]

    张霞, 黄继风 . 结合LBP直方图和SVM的视频火焰检测[J]. 计算机应用与软件, 2016, 33(8): 216-220.

  • [45]

    ZHAO Y , JIA W , RONG X H , et al . Completed robust local binary pattern for texture classification[J]. Neurocomputing, 2013(106): 68-76.

  • [46]

    江帆, 刘辉, 王彬, 等 . 基于火焰图像CNN的转炉炼钢吹炼终点判断方法[J]. 计算机工程, 2016, 42(10): 277-282.

  • [47]

    ZHANG Yanjun , SHAO Mingliang . Video smoke detection based on wavelet texture features[J]. Optical Technique, 2013, 39(4): 348-353.

  • [48]

    胡勤, 陈琛, 刘敏 . 一种基于动态纹理的烟雾和火焰检测方法[J]. 消防科学与技术, 2014, 33(6): 667-669.

  • [49]

    YANG X , WANG J , HE S . A SVM approach for vessel fire detection based on image processing[C]//Proceedings of International Conference on Modelling, Identification & Control. [S.l.]: IEEE, 2012: 150-153.

  • [50]

    LI Kai , LI Shengbo , LIU Rui . Flame detection based on video[J]. Computer Science and Application, 2016, 3(6): 1-7.

  • [51]

    WU Xiyin , YAN Yunyang , DU Jing . Fire detection based on fusion of multiple features[J]. CAAI Transaction on Intelligent System, 2015(10): 240-247.

  • [52]

    仇国庆, 蒋天跃, 冯汉青,等 . 基于火焰尖角特征的火灾图像识别算法[J]. 计算机应用与软件, 2013(12): 52-55.

  • [53]

    SARASWATI S . Identification of flame development and rapid burning angle for SI engine[C]// Proceedings of 2014 Students Conference on Engineering and Systems(SCES). [S.l.]: IEEE, 2014: 22-28.

  • [54]

    YANG Jineng , BU Leping , YANG Zhikai . An early flame identification method based on edge gradient feature[C]// Proceedings of IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. [S.l.]: IEEE, 2018: 642-646.

  • [55]

    石勇, 鲍可进 . 一种基于图像多特征的火焰识别算法[J]. 无线通信技术, 2014, 23(3): 53-58.

  • [56]

    熊国良, 苏兆熙, 刘举平,等 . 火焰特性识别的Matlab实现方法[J]. 计算机工程与科学, 2013, 35(7): 131-136.

  • [57]

    孙建坤, 杨若瑜 . 基于颜色直方图和小波变换的视频烟雾检测[J].计算机科学, 2014, 41(12): 82-88.

  • [58]

    李雪宝,黄徐胜,郑艳芳, 等 . 基于小波变换的森林火灾烟雾检测算法设计[J].信息技术, 2017,10(3): 11-13.

  • [59]

    SUN Jian . Morphological undecimated wavelet decomposition fusion algorithm and its application on fault feature extraction of hydraulic pump[J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2015, 32(3): 268-278.

  • [60]

    CHI Rui , LU Zheming , JI Qingge . Real-time multi-feature based fire flame detection in video[J].IET Image Processing, 2017, 11(1): 31-37.

  • [61]

    ERDEN F , T?REYIN B U , SOYER E B , et al . Wavelet based flame detection using differential PIR sensors[C]//Proceedings of Signal Processing and Communications Applications Conference. [S.l.]: IEEE, 2012: 1-4.

  • [62]

    ZHU R , HU X , TANG J , et al . A novel approach for fire recognition using hybrid features and manifold learning-based classifier[C]//Proceedings of MIPPR 2017: Pattern Recognition and Computer Vision. Wuhai, China: [s.n.], 2017: 10609.

  • [63]

    WU X , LU X , LEUNG H . A video based fire smoke detection using robust AdaBoost[J]. Sensors, 2018, 18(11): 3780.

  • [64]

    CHEN Kang , LI Yaohui , YOU Feng . Smoke detection algorithm about video image with multiple features based on serial and parallel processing model[J]. Computer and Modernization, 2017(4): 1-22.

  • [65]

    LI Shuangqun , WU Liu , MA Huadong . Multi-attribute based fire detection in diverse surveillance videos[C]// Proceedings of International Conference on Multimedia Modeling. [S.l.]: Springer, 2017: 238-250.

  • [66]

    FOGGIA P , SAGGESE A , VENTO M . Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(9): 1545-1556.

  • [67]

    罗胜, Jiang Yuzheng .视频检测烟雾的研究现状[J].中国图象图形学报,2013,18(10): 1225-1236.

  • [68]

    PEREZ A , TABIA H , DECLERCQ D ,et al . Using the conflict in Dempster-Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition[J]. Image & Vision Computing, 2016, 55: 149-157.

  • [69]

    HAN Xianfeng , JIN J S , WANG Mingjie , et al . Video fire detection based on guassian mixture model and multi-color features[J]. Signal, Image and Video Processing, 2017(11): 1419-1425.

  • [70]

    金肖, 叶锦华, 杨素珍 . 多特征融合视频火灾识别研究[J]. 机械制造与自动化, 2019(4): 163-167.

  • [71]

    陈康,李耀华,游峰,等 . 基于串并行处理的多特征交通视频烟雾检测算法[J]. 计算机与现代化,2017,4(1): 1-6.

  • [72]

    张长勇, 吴智博, 杨建忠 . 基于D-S证据理论的飞机火情检测方法[J]. 消防科学与技术, 2018(1): 122-124.

  • [73]

    LIU S , ZHANG Z , QI L . Distribution of primary additional errors in fractal encoding method[J]. Multimedia Tools and Applications, 2017, 76(4): 5787-5802.

  • [74]

    钟玲, 张兴坤 . 基于SVM的视频图像火焰检测[J]. 软件工程, 2017, 20(6): 1-4.

  • [75]

    LIU Li , SHAO Ling , ROCKETT P . Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification[J]. Signal Processing, 2013, 93(6): 1521-1530.

  • [76]

    董力, 陆中, 周伽 . 基于遗传算法的混合威布尔分布参数最小二乘估计[J]. 南京航空航天大学学报, 2019,51(5): 711-718.

  • [77]

    LI K , KONG X , LU Z , et al . Boosting weighted ELM for imbalanced learning[J]. Neurocomputing, 2013, 128(5): 15-21.

  • [78]

    柴瑞敏, 曹振基 . 基于Gabor小波与深度信念网络的人脸识别方法[J]. 计算机应用, 2014, 34(9): 2590-2594.

  • [79]

    SEO J, LI M , KIM C H . An optimal many-core model-based supercomputing for accelerating video-equipped fire detection[J]. Supercomput, 2015(71): 2275-2308.

  • [80]

    PREMA C E , VINSLEY S , SURESH S . Multi feature analysis of smoke in YUV color space for early forest fire detection[J].Fire Technololy,2016. DOI:10.1007/s10694-01-0580-8 .

  • [81]

    王东风, 孟丽 . 粒子群优化算法的性能分析和参数选择[J]. 自动化学报, 2016, 42(10): 1552-1561.

  • [82]

    段锁林, 任珏朋, 毛丹, 等 . 基于改进的PSO优化SVM火灾火焰识别算法研究[J]. 计算机测量与控制, 2016, 24(4): 202-205.

  • [83]

    赵文涛,孟令军,赵好 . 朴素贝叶斯算法的改进与应用[J]. 测控技术,2016, 35(2): 143-147.

  • [84]

    刘臣园, 黄劼, 高小娇 . 基于贝叶斯分类器的林火识别方法研究[J]. 工业控制计算机, 2016, 29(3): 37-38.

  • [85]

    段锁林, 顾川林 . 基于BP神经网络视频火灾火焰检测方法[J]. 常州大学学报:自然科学版, 2017, 29(2): 65-70.

  • [86]

    PREMA C E , VINSLEY S , SURESH S . Efficient flame detection based on static and dynamic texture analysis in forest fire detection[J].Fire Technololy,2018,10(54): 255-288.

  • [87]

    陈娜, 蒋芸, 邹丽 . 基于判别式受限玻尔兹曼机的医学图像分类法[J]. 计算机科学, 2015, 42(5): 315-319.

  • [88]

    PUNDIR A S , RAMAN B . Deep belief network for smoke detection[J]. Fire Technology, 2017(2): 1-18.

  • [89]

    MUHAMMAD K , AHMAD J . Efficient deep CNN-based fire detection and localization in video survellance applications[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(7): 1419-1434.

  • [90]

    LU C , LU M , LU X , et al . Forest fire smoke recognition based on multiple feature fusion[C]// Proceedings of IOP Conference Series: Materials Science and Engineering. [S.l.]: IOP Publishing, 2018, 435 1): 012006.

  • [91]

    钱付兰,李建红,赵姝,等 . 基于深度混合模型评分推荐[J]. 南京航空航天大学学报, 2019,51(5): 592-598.

  • [92]

    WU Hao , LIU Qi , LIU Xiaodong . A review on deep learning approaches to image classification and object segmentation[J]. Tech Science Press,2018(1): 1-22.

  • [93]

    张珊, 逯瑜娇, 罗大为 . 基于深度学习的目标检测算法综述[J]. 计算机科学, 2018, 10(10): 123-135.

  • [94]

    FRIZZI S , KAABI R , BOUCHOUICHA M . Convolutional neural network for video fire and smoke detection[C]//Proceedings of IECON 2016—42nd Annual Conference of IEEE. [S.l.]: IEEE, 2016: 877-882.

  • [95]

    ZHANG Qingjie , XU Jiaolong , XU Liang . Deep convolutional neural network for forest fire detection[C]//Proceedings of International Forum on Management, Education and Information Technology Application. Shenzhen, China: [s.n.], 2016: 568-574.

  • [96]

    REGI M , VARGHESE R G , SIDHARTH V . Deep learning based fire detection system[J]. International Journal of Knowledge Based Computer Systems, 2018, 76(1): 18-22.

  • [97]

    AHMAD J , BELLAVISTA P . Efficient deep CNN-based fire detection and localization in video surveillance applications[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2018(3): 1-13.