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[해외논문] Squeezed fire binary segmentation model using convolutional neural network for outdoor images on embedded device

Machine vision and applications, v.32 no.6, 2021년, pp.120 -   

Song, Kyungmin ,  Choi, Han-Soo ,  Kang, Myungjoo

초록이 없습니다.

참고문헌 (33)

  1. 10.1108/02602281311299635 Bogue, R.: Sensors for fire detection. Sensor Rev. 3(2), 99-103 (2013) 

  2. Thomas K.: Fire detection with temperature sensor arrays. In: Proceedings IEEE 34th Annual 2000 International Carnahan Conference on Security Technology (Cat. No. 00CH37083) 

  3. Fire Saf. J. Shin-Juh Chen 42 8 507 2007 10.1016/j.firesaf.2007.01.006 Chen, Shin-Juh., Hovde, David C., Peterson, Kristen A., Marshall, André W.: Fire detection using smoke and gas sensors. Fire Saf. J. 42(8), 507-515 (2007) 

  4. Fire Technol. Xuanbing Qiu 54 5 1249 2018 10.1007/s10694-018-0727-x Qiu, Xuanbing, Xi, Tingyu, Sun, Dongyuan, Zhang, Enhua, Li, Chuanliang, Peng, Ying, Wei, Jilin, Wang, Gao: Fire detection algorithm combined with image processing and flame emission spectroscopy. Fire Technol. 54(5), 1249-1263 (2018) 

  5. ETRI J. Turgay Celik 32 6 881 2010 10.4218/etrij.10.0109.0695 Celik, Turgay: Fast and efficient method for fire detection using image processing. ETRI J. 32(6), 881-890 (2010) 

  6. IEEE Trans. Ind. Informatics M Khan 15 5 3113 2019 10.1109/TII.2019.2897594 Khan, M., Salman, K., Mohamed, E., Syed, H.A., Sung, W.B.: Efficient fire detection for uncertain surveillance environment. IEEE Trans. Ind. Informatics 15(5), 3113-3122 (2019) 

  7. Fire Technol. Gaohua Lin 55 5 1827 2019 10.1007/s10694-019-00832-w Lin, Gaohua, Zhang, Yongming, Gao, Xu., Zhang, Qixing: Smoke detection on video sequences using 3d convolutional neural networks. Fire Technol. 55(5), 1827-1847 (2019) 

  8. Süleyman, A., Uğur, G., B Uğur, T., Enis Çetin, A.: Deep convolutional generative adversarial networks based flame detection in video. arXiv:1902.01824 (2019) 

  9. 10.1016/j.comcom.2019.10.007 Sudhakar, S., Varadarajan Vijayakumar, C., Sathiya Kumar, V., Priya., Logesh, R., Subramaniyaswamy, V, : Unmanned aerial vehicle (uav) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 149, 1-16 (2020) 

  10. 10.1109/ICCPCT.2014.7054883 Emmy Premal, C., Vinsley, S.S.: Image processing based forest fire detection using ycbcr colour model. Presented at the (2014) 

  11. Viktor, T., Romana, C.-H., Eva, T.: Forest fires detection in digital images based on color features. Int. J. Educ. Learn. Syst., 2, 66-70 (2017) 

  12. 10.1007/978-3-319-10602-1_48 Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Lawrence, C., Zitnick. (eds.): In: Microsoft coco: Common objects in context, pp. 740-755. Springer (2014) 

  13. Yuval N., Iacopo, M., Anh Tran, T., Tal, H., Gerard, M.: On face segmentation, face swapping, and face perception. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 98-105. IEEE (2018) 

  14. Remote Sens. Lett. Ye Li 10 4 381 2019 10.1080/2150704X.2018.1557791 Li, Ye., Lele, Xu., Rao, Jun, Guo, Lili, Yan, Zhen, Jin, Shan: A y-net deep learning method for road segmentation using high-resolution visible remote sensing images. Remote Sens. Lett. 10(4), 381-390 (2019) 

  15. ISPRS J. Photogramm. Remote Sens. W Michael 150 59 2019 10.1016/j.isprsjprs.2019.02.006 Michael, W., Thomas, S., Xiao, X.Z., Matthias, W., Hannes, T.: Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS J. Photogramm. Remote Sens. 150, 59-69 (2019) 

  16. 10.1109/TMM.2020.2967645 Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3d room layout estimation from a single rgb image. IEEE Trans. Multimed. 22(11), 3014-3024 (2020) 

  17. 10.1007/978-3-319-24574-4_28 Ronneberger, O., Fischer, P., Brox, T.: In: U-net: convolutional networks for biomedical image segmentation, pp. 234-241. Springer (2015) 

  18. Tran, M.Q., David, G.C.H, Won-Ki, J.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics. arXiv:1612.05360 (2016) 

  19. Med. Image Anal. Michal Drozdzal 44 1 2018 10.1016/j.media.2017.11.005 Drozdzal, Michal, Chartrand, Gabriel, Vorontsov, Eugene, Shakeri, Mahsa, Di Jorio, Lisa, Tang, An., Romero, Adriana, Bengio, Yoshua, Pal, Chris, Kadoury, Samuel: Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image Anal. 44, 1-13 (2018) 

  20. 10.1109/CVPR.2015.7298965 Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. Presented at the (2015) 

  21. 10.1109/IPSN.2016.7460664 Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., Kawsar, F., Deepx, (eds.): A software accelerator for low-power deep learning inference on mobile devices. Presented at the (2016) 

  22. Cazzolato, M.T., Avalhais, L.P.S., Chino, D.Y.T., Ramos, J.S., de Souza, J.A., Rodrigues-Jr, Jose, F., Traina, A.J.: Fismo: a compilation of datasets from emergency situations for fire and smoke analysis. Proc. Satell. events (2017) 

  23. Fire Saf. J. T Tom 92 188 2017 10.1016/j.firesaf.2017.06.012 Tom, T., Lucile, R., Antoine, C., Turgay, C., Moulay Akhloufi, A.: An evolving image dataset for processing and analysis. Computer vision for wildfire research. Fire Saf. J. 92, 188-194 (2017) 

  24. Fire Saf. J. Turgay Celik 44 2 147 2009 10.1016/j.firesaf.2008.05.005 Celik, Turgay, Demirel, Hasan: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147-158 (2009) 

  25. 10.1109/ICCV.2015.178 Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. Presented at the (2015) 

  26. 10.1007/978-3-319-46976-8_19 Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications, pp. 179-187. Springer (2016) 

  27. Neurocomputing Feiniu Yuan 357 248 2019 10.1016/j.neucom.2019.05.011 Yuan, Feiniu, Zhang, Lin, Xia, Xue, Wan, Boyang, Huang, Qinghua, Li, Xuelong: Deep smoke segmentation. Neurocomputing 357, 248-260 (2019) 

  28. Forrest, N.I., Song, H., Matthew, W.M., Khalid, A., William, J.D., Kurt, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv:1602.07360 (2016) 

  29. Andrew, G.H., Menglong, Z., Bo, C., Dmitry, K., Weijun, W., Tobias, W., Marco, A., Hartwig, A.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017) 

  30. 10.1109/SII46433.2020.9026244 Novac, I., Geipel K.R., de Domingo Gil, J.E., de Paula, L.G., Hyttel, K., Chrysostomou, D.: A framework for wild-re inspection using deep convolutional neural networks. In: 2020 IEEE/SICE International Symposium on System Integration (SII), pp 867-872. IEEE (2020) 

  31. 10.1109/CVPR.2018.00474 Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C..: Mobilenetv 2: inverted residuals and linear bottlenecks. Presented at the (2018) 

  32. 10.1007/978-3-319-10590-1_53 Zeiler, M.D., Fergus, R.: In: Visualizing and understanding convolutional networks, pp. 818-833. Springer (2014) 

  33. 10.1109/CVPR.2017.195 Chollet, F.: Xception: deep learning with depthwise separable convolutions. Presented at the (2017) 

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