Changlin Song, Chuanfu Liu, Tingping Feng, Junmin Li, Yanlin Pan, and Simon X. Yang


  1. [1] J. Shi, F. Yuan, and X. Xia, Video smoke detection: a literaturesurvey, Journal of Image and Graphics, 23(3), 2018.
  2. [2] C. Emmy Prema, S. Vinsley, and S. Suresh, Multi featureanalysis of smoke in YUV color space for early forest firedetection, Fire Technology, 52(5), 2016, 1319-1342.
  3. [3] T. Chen, Y. Yin, S. Huang, and Y. Ye, The smoke detectionfor early fire-alarming system base on video processing,International Conference on Intelligent Information Hidingand Multimedia, ( Pasadena, CA, USA, 2006), 427-430.
  4. [4] L. Dong, and J. YU, Smoke detection method in video based onimage separation, Computer engineering, 41(9), 2015, 251-254.
  5. [5] K. Nikhil, Convolutional Neural Networks, Deep Learning withPython: A Hands-on Introduction, 2017, 63-78.
  6. [6] Z. Yin, B. Wan, F. Yuan, X. Xia, and J. Shi, A deepnormalization and convolutional neural network for imagesmoke detection, IEEE Access, 5(5), 2017, 18429-18438.
  7. [7] Y. Hu, and X. Lu, Real-time video fire smoke detection byutilizing spatial-temporal ConvNet features, Multimedia Toolsand Applications, 77(22), 2018, 29283-29301.
  8. [8] G. Xu, Y. Zhang, Q. Zhang, G. Lin, Z. Wang, Y. Jia, J. Wang,Video smoke detection based on deep saliency network, FireSafety Journal, 105(105), 2019, 277-285.
  9. [9] F. Yuan, L. Zhang, X. Xia, B. Wan, Q. Huang, and X. Li, Deepsmoke segmentation, Neurocomputing, 357(357), 2019,248-260.
  10. [10] Q. Zhang, G. Lin, Y. Zhang, G. Xu, and J. Wang, Wildlandforest fire smoke detection based on faster R-CNN usingsynthetic smoke images, Procedia Engineering, 211(211), 2018,441-446.
  11. [11] G. Lin, Y. Zhang, G. Xu, and Q. Zhang, Smoke detection onvideo sequences using 3D convolutional neural networks, FireTechnology, 55(5), 2019, 1827-1847.
  12. [12] C.M. Li, X.Y. Qu, Y. Yang, H.M. Gao, Y.C. Wang, et al., High-Resolution remote sensing image segmentation method basedon SReLU. International Journal of Robotics and Automation,34(3), 2019, 225-234.
  13. [13] C.M. Li, H.M. Gao, Y. Yang, X.Y. Qu, and W.J. Yuan.Segmentation method of High-Resolution remote sensing imagefor fast object detection, International Journal of Robotics andAutomation, 34(3), 2019, 216-224.
  14. [14] P. Liu, C.L. Song, J.M. Li, X.Y. Simon, C. Xingyu, L. Chuanfu,F. Qiang, Detection of transmission line against external forcedamage based on improved yolov3, International Journal ofRobotics and Automation, 35(6), 2020, 460-468.
  15. [15] T.D. Dung, G. Capi. Application of neural networks forrobot 3D mapping and annotation using depth image camera,International Journal of Robotics and Automation, 37(6), 2022,529-536.168
  16. [16] S. Woo, J. Park, and J.Y. Lee, CBAM: Convolutional BlockAttention Module, european conference on computer vision,2018, 3-19.
  17. [17] J. Hu, L. Shen, and S. Albanie, Squeeze-and-Excitationnetworks, IEEE Transactions on Pattern Analysis and MachineIntelligence, 42(8), 2020, 2011-2023.
  18. [18] X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J.Liang, EAST: An Efficient and Accurate Scene Text Detector,computer vision and pattern detection, 2017, 2642-2651.
  19. [19] R.C. Gonzalez, and R.E. Woods, Digital image processing,3th ed, (Chain: Ruan Qiuqi, Electronic Industry Press, 2011),66-435.
  20. [20] X. Qin, C. Yuan, Y. Deng, Y. Shi, and J. Yuan, Animproved Ostu image segmentation method, Journal of ShanxiUniversity, 36(4), 2013, 530-534.
  21. [21]
  22. [22]

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