[1] Du S Y, Liu Z G. A comparative study of different color spaces in computer-vision-based flame detection[J]. Multimedia Tools and Applications, 75, 10291-10310(2016). http://dl.acm.org/doi/10.1007/s11042-015-2990-x

[2] Qiu G Q, Liu S, Cao D M et al. Materials, 687/688/689/690/691, 3604-3607(2014).

[3] Zhang D D, Zhang G, Chen X J et al. Flame identification algorithm based on improved multi-feature fusion of YCbCr and region growth[J]. Laser & Optoelectronics Progress, 57, 061022(2020).

[4] Dimitropoulos K, Barmpoutis P, Grammalidis N. Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 25, 339-351(2015).

[5] Zhao Q, Sun F D, Li W H et al. Flame detection using generic color model and improved block-based PCA in active infrared camera[J]. International Journal of Pattern Recognition and Artificial Intelligence, 32, 1850014(2018). http://smartsearch.nstl.gov.cn/paper_detail.html?id=ad6bf635d4a9f81913399f88a4702b01

[6] Kong S G, Jin D L, Li S Z et al. Fast fire flame detection in surveillance video using logistic regression and temporal smoothing[J]. Fire Safety Journal, 79, 37-43(2016).

[7] Simonyan K. -04-10)[2020-07-01]. https:∥arxiv., org/abs/1409, 1556(2015).

[8] Luo Y M, Zhao L, Liu P Z et al. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks[J]. Multimedia Tools and Applications, 77, 15075-15092(2018).

[9] Muhammad K, Ahmad J, Mehmood I et al. Convolutional neuralnetworks based fire detection in surveillance videos[J]. IEEE Access, 6, 18174-18183(2018). http://ieeexplore.ieee.org/document/8307064/

[10] Hong W, Li C F. Flame detection method based on regional fully convolutional networks with residual network[J]. Laser & Optoelectronics Progress, 55, 041011(2018).

[11] Maksymiv O, Rak T, Peleshko D. Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence. [C]∥2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), February 21-25, 2017, Lviv, Ukraine. New York: IEEE, 351-353(2017).

[12] Yang H J, Jang H, Kim T et al. Non-temporal lightweight fire detection network for intelligent surveillance systems[J]. IEEE Access, 7, 169257-169266(2019).

[13] Szegedy C, Liu W, Jia Y Q 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, 15523970(2015).

[14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[15] Li Y Y, Xu Y L, Ma S P et al. Object recognition method imitating human brain visual cortex-like mechanisms[J]. Computer Engineering and Design, 36, 2147-2151, 2216(2015).

[16] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[17] van den Bergh M, Boix X, Roig G et al. SEEDS: superpixels extracted via energy-driven sampling[M]. ∥Fitzgibbon A, Lazebnik S, Perona P, et al. Computer vision-ECCV 2012. Lecture notes in computer science. Heidelberg: Springer, 7578, 13-26(2012).

[18] Steffens C R, Rodrigues R N. Costa Botelho S S. Non-stationary VFD evaluation kit: dataset and metrics to fuel video-based fire detection development[M]. ∥Santos Osório F, Sales Gonçalves R. Robotics. Communications in computer and information science. Cham: Springer, 619, 135-151(2016).

[19] Li Z R, Wang H Q, Hu Y et al. Flame image detection method based on deep learning with maximal relevance and minimal redundancy[J]. Laser & Optoelectronics Progress, 57, 101015(2020).

[20] Fu Y J, Zhang H L. Forest fire detection method based on transfer learning of convolutional neural network[J]. Laser & Optoelectronics Progress, 57, 041010(2020).