面向视频图像的烟雾检测算法综述 下载: 1853次
陈长友, 杨健晟. 面向视频图像的烟雾检测算法综述[J]. 激光与光电子学进展, 2021, 58(4): 0400003.
Changyou Chen, Jiansheng Yang. Review on Smoke Detection Algorithms for Video Images[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0400003.
[1] 相徐斌. 基于视频的烟雾检测算法研究[D]. 杭州:浙江大学, 2017.
Xiang XB. Research on smoke detection algorithm based on video[D]. Hangzhou: Zhejiang University, 2017.
[2] 肖潇, 孔凡芝, 刘金华. 基于动静态特征的监控视频火灾检测算法[J]. 计算机科学, 2019, 46(S1): 284-286, 299.
[3] Uğur TÖreyinB, DedeoğluY, Enis Çetin A. Wavelet based real-time smoke detection in video[C] ∥ 2005 13th European Signal Processing Conference, September 4-8, 2005,Antalya, Turkey. New York: IEEE, 2005: 15037050.
[4] 周泊龙, 宋英磊, 俞孟蕻. 基于图像处理的火灾烟雾检测算法研究[J]. 消防科学与技术, 2016, 35(3): 390-393.
[5] WangL, Li AG. Early fire recognition based on multi-feature fusion of video smoke[C]∥2017 36th Chinese Control Conference (CCC), July 26-28, 2017, Dalian, China. New York: IEEE, 2017: 5318- 5323.
[6] Hu Y C, Lu X B. Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features[J]. Multimedia Tools and Applications, 2018, 77(22): 29283-29301.
[7] 曹江涛, 秦跃雁, 姬晓飞. 基于视频的火焰检测算法综述[J]. 数据采集与处理, 2020, 35(1): 35-52.
[9] Agrawal S, Panda R, Bhuyan S, et al. Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm[J]. Swarm and Evolutionary Computation, 2013, 11: 16-30.
[12] 章三妹, 唐正明. 基于HIS颜色空间的彩色图像分割算法研究[J]. 西华师范大学学报(自然科学版), 2012, 33(3): 304-307.
[13] ChmelarP, BenkridA. Efficiency of HSV over RGB Gaussian mixture model for fire detection[C]∥2014 24th International Conference Radioelektronika, April 15-16, 2014, Bratislava, Slovakia. New York: IEEE, 2014: 1- 4.
[14] Appana DK, IslamR, Khan SA, et al. and spatial-temporal energy analyses for alarm systems[J]. Information Sciences, 2017, 418/419: 91- 101.
[15] Emmy Prema C, Vinsley S S, Suresh S. Multi feature analysis of smoke in YUV color space for early forest fire detection[J]. Fire Technology, 2016, 52(5): 1319-1342.
[16] 张丹丹, 章光, 陈西江, 等. 改进YCbCr和区域生长的多特征融合的火焰精准识别算法[J]. 激光与光电子学进展, 2020, 57(6): 061022.
[17] 赵战民, 朱占龙, 刘永军, 等. 对类大小不敏感的图像分割模糊C均值聚类方法[J]. 激光与光电子学进展, 2020, 57(2): 021001.
[18] Tian HD, Li WQ, WangL, et al. A novel video-based smoke detection method using image separation[C]∥2012 IEEE International Conference on Multimedia and Expo, July 9-13, 2012, Melbourne, VIC, Australia. New York: IEEE, 2012: 532- 537.
[19] 魏玮, 吴琪. 三帧差结合改进高斯建模的运动目标检测算法[J]. 计算机工程与设计, 2014, 35(3): 949-952.
[22] MurgiaJ, MeurieC, RuichekY. An improved colorimetric invariants and RGB-depth-based codebook model for background subtraction using Kinect[M] ∥ Gelbukh A, Espinoza F C, Galicia-Haro S N. Human-inspired computing and its applications. MICAI 2014. Lecture notes in computer science. Cham: Springer, 2014, 8856: 380- 392.
[23] Huang W, Liu L, Yue C, et al. The moving target detection algorithm based on the improved visual background extraction[J]. Infrared Physics & Technology, 2015, 71: 518-525.
[24] 阳婷, 官洪运. 基于帧间高频能量和相关性的烟雾检测算法研究[J]. 微型机与应用, 2015, 34(17): 32-35.
[25] 马永杰, 陈梦利, 刘培培, 等. ViBe算法鬼影抑制方法研究[J]. 激光与光电子学进展, 2020, 57(2): 021007.
[27] Cheng YH, Wang J. A motion image detection method based on the inter-frame difference method[J]. Applied Mechanics and Materials, 2014, 490/491: 1283- 1286.
[29] 刘恺, 刘湘, 常丽萍, 等. 基于YUV颜色空间和多特征融合的视频烟雾检测[J]. 传感技术学报, 2019, 32(2): 237-243.
[31] 胡学龙. 数字图像处理[M]. 2版. 北京: 电子工业出版社, 2011.
Hu XL. Digital image processing[M]. 2nd ed. Beijing: Publishing House of Electronics industry, 2011.
[32] 张德丰著. 数字图像处理(MATLAB版)[M]. 第二版.北京: 人民邮电出版社, 2015.
Zhang DF. Digital image processing(MATLAB edition)[M]. 2nd ed. Beijing: Posts and Telecom Press, 2015.
[33] Chiranjeevi P, Sengupta S. Moving object detection in the presence of dynamic backgrounds using intensity and textural features[J]. Journal of Electronic Imaging, 2011, 20(4): 043009.
[35] 李巨虎, 范睿先, 陈志泊. 基于颜色和纹理特征的森林火灾图像识别[J]. 华南理工大学学报(自然科学版), 2020, 48(1): 70-83.
Li J H, Fan R X, Chen Z B. Forest fire recognition based on color and texture features[J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(1): 70-83.
[38] 刘操, 郑宏, 黎曦, 等. 基于多通道融合HOG特征的全天候运动车辆检测方法[J]. 武汉大学学报·信息科学版, 2015, 40(8): 1048-1053.
Liu C, Zheng H, Li X, et al. A method of moving vehicle detection in all-weather based on melted multi-channel HOG feature[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1048-1053.
[39] Wang Y, Li M, Zhang C X, et al. Weighted-fusion feature of MB-LBPUH and HOG for facial expression recognition[J]. Soft Computing, 2020, 24(8): 5859-5875.
[40] 岳姣姣. 基于多特征融合的林火烟雾检测算法研究[D]. 秦皇岛: 燕山大学, 2016.
Qiu JJ. The research on forest fire smoke detection algorithm based on feature fusion[D]. Qinhuangdao: Yanshan University, 2016.
[42] Luo S, Yan C W, Wu K L, et al. Smoke detection based on condensed image[J]. Fire Safety Journal, 2015, 75: 23-35.
[43] 殷梦霞, 王理, 孙连营. 基于多特征融合的自适应烟雾检测算法[J]. 建筑科学, 2019, 35(9): 26-31.
[44] CaiM, Lu XB, Wu XH, et al. Intelligent video analysis-based forest fires smoke detection algorithms[C]∥2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), August 13-15, 2016, Changsha, China. New York: IEEE, 2016: 1504- 1508.
[47] 邓林. 视频烟雾检测算法研究[D]. 成都:电子科技大学, 2015.
DengL. Study on video smoke detection algorithm[D]. Chengdu: University of Electronic Science and Technology of China, 2015.
[48] Lee C, Lin C, Hong C, et al. Smoke detection using spatial and temporal analyses[J]. International Journal of Innovative Computing Information and Control, 2012, 8: 4749.
[50] Wang T, Liu Y, Xie Z P. A new video smoke detection method based on wave analysis[J]. Electronics and Information Journal, 2011, 33(5): 1024-1029.
[53] Hu Y C, Lu X B. Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features[J]. Multimedia Tools and Applications, 2018, 77(22): 29283-29301.
[55] Xu ZG, Xu JL. Automatic fire smoke detection based on image visual features[C]∥2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007), December 15-19, 2007, Heilongjiang, China. New York: IEEE, 2007: 316- 319.
[56] CuiY, DongH, Zhou EZ. An early fire detection method based on smoke texture analysis and discrimination[C]∥2008 Congress on Image and Signal Processing, May 27-30, 2008, Sanya, Hainan, China. New York: IEEE, 2008: 95- 99.
[57] Srisuwan T, Ruchanurucks M. Smoke detection using GLCM, wavelet, and motion[J]. Proceedings of SPIE, 2014, 9069: 90691H.
[59] Chen JZ, You Y. Early fire detection using HEP and space-timeanalysis[EB/OL].( 2013-10-07)[2020-05-13]. https:∥arxiv.org/abs/1310. 1855.
[60] Wang YB. Smoke recognition based on machine vision[C]∥2016 International Symposium on Computer, Consumer and Control (IS3C), July 4-6, 2016, Xi'an, China. New York: IEEE, 2016: 668- 671.
[61] Ye W, Zhao JH, Wang S, et al. Dynamic texture based smoke detection using surfacelet transform and HMT model[J]. Fire Safety Journal, 2015, 73: 91-101.
[63] Yuan F N. A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection[J]. Pattern Recognition, 2012, 45(12): 4326-4336.
[64] MarutaH, IidaY, KurokawaF. Smoke detection method using Local Binary Patterns and AdaBoost[C]∥2013 IEEE International Symposium on Industrial Electronics, May 28-31, 2013, Taipei, Taiwan, China. New York: IEEE, 2013: 1- 6.
[65] Zhao Y Q, Li Q J, Gu Z. Early smoke detection of forest fire video using CS Adaboost algorithm[J]. Optik - International Journal for Light and Electron Optics, 2015, 126(19): 2121-2124.
[66] 谢振平, 王涛, 刘渊. 一种集成贝叶斯决策的视频烟雾检测新方法[J]. 计算机工程与应用, 2014, 50(3): 173-176.
Xie Z P, Wang T, Liu Y. New video smoke detection method using Bayesian decision[J]. Computer Engineering and Applications, 2014, 50(3): 173-176.
[67] 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- 6, 22.
ChenK, Li YH, YouF, et al. Smoke detection algorithm about video image with multiple features based on serial and parallel processing model[J]. Computer and Modernization, 2017( 4): 1- 6, 22.
[69] 姚丽莎, 徐国明, 赵凤. 基于卷积神经网络局部特征融合的人脸表情识别[J]. 激光与光电子学进展, 2020, 57(4): 041513.
[70] 赵亮. 基于视频的火灾烟雾检测算法的研究[D]. 泉州: 华侨大学, 2017.
ZhaoL. Research of fire smoke detection algorithm based on video[D]. Quanzhou: Huaqiao University, 2017.
[71] KrizhevskyA, HintonG. Convolutional deep belief networks on cifar-10[EB/OL]. [2020-05-13].https:∥www.cs.toronto.edu/~kriz/conv-cifar10-aug2010.pdf.
[72] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[73] Zeiler MD, FergusR. Visualizing and understanding convolutional networks[M] ∥ Fleet D, Pajdla T, Schiele B, et al. Computer Vision - ECCV 2014. Lecture Notes in Computer Science.Cham: Springer, 2014, 8689: 818- 833.
[74] SzegedyC, LiuW, JiaY, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1- 9.
[75] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL].( 2014-09-04)[2020-05-13]. https:∥arxiv.org/abs/1409. 1556.
[76] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.
[78] LiangM, Hu XL. Recurrent convolutional neural network for object recognition[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3367- 3375.
[79] 冯路佳, 王慧琴, 王可, 等. 基于目标区域的卷积神经网络火灾烟雾识别[J]. 激光与光电子学进展, 2020, 57(16): 161004.
[80] 曹诗雨, 刘跃虎, 李辛昭. 基于Fast R-CNN的车辆目标检测[J]. 中国图象图形学报, 2017, 22(5): 671-677.
[81] Ren S Q, He K M, Girshick R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[82] 李珣, 时斌斌, 刘洋, 等. 基于改进YOLOv2模型的多目标识别方法[J]. 激光与光电子学进展, 2020, 57(10): 101010.
[83] RedmonJ, Farhadi A.YOLO9000:better, faster, stronger[EB/OL].( 2016-12-25)[2020-05-13]. https:∥arxiv.org/abs/1612. 08242.
[84] RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 779- 788.
[85] 杜晨锡, 严云洋, 刘以安, 等. 基于YOLOv2的视频火焰检测方法[J]. 计算机科学, 2019, 46(6): 301-304.
Du C X, Yan Y Y, Liu Y A, et al. Video fire detection method based on YOLOv2[J]. Computer Science, 2019, 46(6): 301-304.
[86] 刘学平, 李玙乾, 刘励, 等. 嵌入SENet结构的改进YOLOV3目标识别算法[J]. 计算机工程, 2019, 45(11): 243-248.
[87] 程淑红, 马继勇, 张仕军, 等. 改进的混合高斯与YOLOv2融合烟雾检测算法[J]. 计量学报, 2019( 5): 798- 803.
Cheng SH, Ma JY, Zhang SJ, et al. Smoke detection algorithm combined with improved Gaussian mixture and YOLOv2[J]. Acta Metrologica Sinica, 2019( 5): 798- 803.
[88] 谭威, 周斌, 张辉, 等. 基于SSD的实时车辆检测算法研究[J]. 计算机与数字工程, 2019, 47(11): 2763-2766.
[91] Tao CY, ZhangJ, WangP. Smoke detection based on deep convolutional neural networks[C]∥2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), December 3-4, 2016, Wuhan, China. New York: IEEE, 2016: 150- 153.
[93] 万博洋. 基于深度学习的烟雾检测算法研究[D]. 南昌:江西科技师范大学, 2018.
Wan BY. Study on smoke detection algorithm based on deep learning[D]. Nanchang: Jiangxi Science and Technology Normal University, 2018.
[94] 冯建新, 李慧, 刘治国. 新型火焰颜色空间: IFCS[J]. 计算机工程与应用, 2019, 55(5): 203-210, 264.
Feng J X, Li H, Liu Z G. New flame color space: IFCS[J]. Computer Engineering and Applications, 2019, 55(5): 203-210, 264.
陈长友, 杨健晟. 面向视频图像的烟雾检测算法综述[J]. 激光与光电子学进展, 2021, 58(4): 0400003. Changyou Chen, Jiansheng Yang. Review on Smoke Detection Algorithms for Video Images[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0400003.