GAUR A, SINGH A, KUMAR A, et al. Video flame and smoke based fire detection algorithms: a literature review[J]. Fire technology, 2020, 56: 1943–1980.

Article  Google Scholar 

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.

Article  Google Scholar 

WANG T, BU L, YANG Z, et al. A new fire detection method using a multi-expert system based on color dispersion, similarity and centroid motion in indoor environment[J]. IEEE/CAA journal of automatica sinica, 2019, 7(1): 263–275.

Google Scholar 

XIONG W. Research on fire detection and image information processing system based on image processing[C]//2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI), September 12–13, 2020, Ottawa, ON, Canada. New York: IEEE, 2020: 106–109.

Google Scholar 

LI C, CAO Y, PENG Y. Research on automatic driving target detection based on YOLOv5s[C]//Journal of Physics: Conference Series 2022, November 12–14, 2021, Beihai, Guangxi, China. Bristol: IOP Publishing, 2022, 2171(1): 012047.

Google Scholar 

LI S, LI L. DRT-Unet: a segmentation network for aiding brain tumor diagnosis[J]. Security & communication networks, 2022.

REN S, HE K, GIRSHICK R, et al. Faster R-CNN towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//2016 European Conference on Computer Vision (ECCV), October 11–14, 2016, Amsterdam, Netherlands. Cham: Springer International Publishing, 2016: 21–37.

Google Scholar 

REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 26-July 1, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 779–788.

Google Scholar 

LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, October 22–29, 2017, Venice, Italy. New York: IEEE, 2017: 2980–2988.

Google Scholar 

LINT Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, Hawaii, USA. New York: IEEE, 2017: 2117–2125.

Google Scholar 

LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition, June 18–22, 2018, Salt Lake City, Utah, USA. New York: IEEE, 2018: 8759–8768.

Google Scholar 

REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, Hawaii, USA. New York: IEEE, 2017: 7263–7271.

Google Scholar 

REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2023-01-14]. http://arxiv.org/abs/1804.02767.

BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-05-28) [2023-01-14]. http://arxiv.org/abs/2004.10934.

CHAOXIA C, SHANG W, ZHANG F. Information-guided flame detection based on faster R-CNN[J]. IEEE access, 2020, 8: 58923–58932.

Article  Google Scholar 

SHI L, ZHANG H F, YANG J F. Video-based fire and smoke detection based on improved SSD[J]. Computer applications and software, 2021, 38(12): 161–167. (in Chinese)

Google Scholar 

HU Y, ZHAN J, ZHOU G, et al. Fast forest fire smoke detection using MVMNet[J]. Knowledge-based systems, 2022, 241: 108219.

Article  Google Scholar 

CAI W, WANG C, HUANG H, et al. A real-time smoke detection model based on YOLO-SMOKE algorithm[C]//2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC), December 17–18, 2020, Fuzhou, China. New York: IEEE, 2020: 1–3.

Google Scholar 

ZHANG S G, ZHANG F, DING Y, et al. Swin-YOLOv5: research and application of fire and smoke detection algorithm based on YOLOv5[J]. Computational intelligence and neuroscience, 2022.

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.

HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap operations[C]//Proceedings of 2020 IEEE Conference on Computer Vision and Pattern Recognition, June 14–19, 2020, Virtual. New York: IEEE, 2020: 1580–1589.

Google Scholar 

HOWARD A G, ZHU M, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-06-18) [2023-01-14]. http://arxiv.org/abs/1704.04861.

LI D, HU J, WANG C, et al. Involution: inverting the inherence of convolution for visual recognition[C]//Proceedings of 2021 IEEE Conference on Computer Vision and Pattern Recognition, June 19–25, 2021], Virtual. New York: IEEE, 2021: 12321–12330.

Google Scholar 

AVENASH R, VISWANATH P. Semantic segmentation of satellite images using a modified CNN with hard-swish activation function[C]//Proceedings of 2019 the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 25–-27, 2019, Prague, Czech Republic. Seattle: Semantic Scholar, 2019: 413–420.

Google Scholar 

MA N, ZHANG X, SUN J. Funnel activation for visual recognition[C]//2020 European Conference on Computer Vision (ECCV), August 23–28, 2020, Glasgow, UK. Cham: Springer International Publishing, 2020: 351–368.

Google Scholar 

GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//Proceedings of 2011 the 14th International Conference on Artificial Intelligence and Statistics, April 11–13, 2011, Ft. Lauderdale, USA. Cambridge: JMLR, 2011: 315–323.

Google Scholar 

MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: practical guidelines for efficient CNN architecture design[C]//2018 European Conference on Computer Vision (ECCV), September 8–14, 2018, Munich, Germany. Cham: Springer International Publishing, 2018: 116–131.

Google Scholar 

HOWARD A, SANDLER M, CHU G, et al. Searching for mobilenetv3[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, October 27–November 2, 2019, Seoul, Korea. New York: IEEE/CVF, 2019: 1314–1324.

Google Scholar 

Fire-flame-dataset: version 1.0[EB/OL]. (2019-06-28) [2023-01-14]. https://github.com/DeepQuestAI/Fire-Smoke-Dataset.

Fire-smoke-detect-YOLOv4-v5 and fire-smoke-detect-dataset: version 1.0[EB/OL]. (2022-12-03) [2023-01-14]. https://github.com/gengyanlei/fire-smoke-detect-yolov4.