• Laser & Optoelectronics Progress
  • Vol. 57, Issue 20, 201021 (2020)
Hong Zhang1、2, Yunyang Yan1、2、*, Yian Liu1, and Shangbing Gao2
Author Affiliations
  • 1School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu 223003, China;
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    DOI: 10.3788/LOP57.201021 Cite this Article Set citation alerts
    Hong Zhang, Yunyang Yan, Yian Liu, Shangbing Gao. Fire Detection Method Based on Localization Confidence and Region-Based Fully Convolutional Network[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201021 Copy Citation Text show less
    References

    [1] Esfahlani S S. Mixed reality and remote sensing application of unmanned aerial vehicle in fire and smoke detection[J]. Journal of Industrial Information Integration, 15, 42-49(2019). http://www.sciencedirect.com/science/article/pii/S2452414X18300773

    [2] Qureshi W S, Ekpanyapong M, Dailey M N et al. QuickBlaze: early fire detection using a combined video processing approach[J]. Fire Technology, 52, 1293-1317(2016). http://link.springer.com/article/10.1007/s10694-015-0489-7

    [3] Frizzi S, Kaabi R, Bouchouicha M et al. Convolutional neural network for video fire and smoke detection[C]∥IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. 23-26 Oct. 2016, Florence, Italy., 877-882(2016).

    [4] Hao G, Yang Y K, Yi Q. General target detection method based on improved SSD[C]∥2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 24-26 May 2019, Chongqing, China., 1787-1791(2019).

    [5] Lan W B, Dang J W, Wang Y P et al. Pedestrian detection based on YOLO network model[C]∥2018 IEEE International Conference on Mechatronics and Automation (ICMA). 5-8 Aug. 2018, Changchun, China., 1547-1551(2018).

    [6] Du C X, Yan Y Y, Liu Y A et al. Video fire detection method based on YOLOv2[J]. Computer Science, 46, 301-304(2019).

    [7] Wu S, Zhang L. Using popular object detection methods for real time forest fire detection. [C]∥2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE(2019).

    [8] Hui T[J]. Halidan·Abudureyimu, Du H. Multi-type flame detection combined with Faster R-CNN Journal of Image and Graphics, 2019, 73-83.

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

    [10] 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, 39, 1137-1149(2017).

    [11] Jiang B R, Luo R X, Mao J Y et al[M]. Acquisition of localization confidence for accurate object detection, 816-832(2018).

    [12] Zhou R G, Cheng Y, Liu D Q. Quantum image scaling based on bilinear interpolation with arbitrary scaling ratio[J]. Quantum Information Processing, 18, 1-19(2019).

    [13] Ji X S, Wang H. Head detection method based on optimized deformable regional fully convolutional neutral netw orks[J]. Laser & Optoelectronics Progress, 56, 141009(2019).

    [14] Elizabeth M M. Self-regulatory organizations; NYSE Arca, Inc.; Order granting an extension to limited exemption from rule 612(c) of regulation NMS in connection with the exchange's retail liquidity program until september 30, 2019[J]. Federal Register FIND, 84, 192(2019).

    [15] Chen Z, Ye X Y, Qian D W et al. Small-scale pedestrian detection based on improved Faster R-CNN[J]. Computer Engineering, 46, 226-232, 241(2020).

    [16] Krittayanawach N, Vateekul P. Robust compression technique for YOLOv3 on real-time vehicle detection[C]∥2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). 10-11 Oct. 2019, Pattaya, Th, 1-6(2019).

    Hong Zhang, Yunyang Yan, Yian Liu, Shangbing Gao. Fire Detection Method Based on Localization Confidence and Region-Based Fully Convolutional Network[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201021
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