Abstract
Traditional flame and smoke detection mostly rely on temperature and smoke sensor, but the detection of temperature detector and smoke detector has a certain lag. In order to solve this problem of hysteresis and low accuracy, we propose an improved YOLOV3 algorithm combined with attention mechanism for flame and smoke detection. Firstly, a multi-scene large-scale flame and smoke image dataset is built. The localization and classification of the flame and smoke areas in the image are annotated precisely. The suspected areas of the flame and smoke in the image are obtained by color analysis, so that the suspected areas of the flame and smoke objects are concerned. Then combined with the feature extraction ability of deep network, the problem of flame and smoke detection is transformed into multi-classification and coordinate regression. Finally, the detection model of flame and smoke in multi-scene is obtained. Our experiments show the effectiveness of the improved YOLOv3 algorithm combined with attention mechanism in flame and smoke detection. Our proposed method achieves outstanding performance in the dataset of flame and smoke image. The detection speed also meets the need of real-time detection.
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Li, Z., Mihaylova, L.S., Isupova, O., Rossi, L.: Autonomous flame detection in videos with a dirichlet process Gaussian mixture color model. IEEE Trans. Ind. Inf. 14(3), 1146–1154 (2017)
Prema, C.E., Vinsley, S.S., Suresh, S.: Efficient flame detection based on static and dynamic texture analysis in forest fire detection. Fire Technol. 54(1), 255–288 (2018)
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. IEEE Trans. Circ. Syst. Video Technol. 25(9), 1545–1556 (2015)
Han, X.-F., Jin, J.S., Wang, M.-J., Jiang, W., Gao, L., Xiao, L.-P.: Video fire detection based on Gaussian mixture model and multi-color features. SIViP 11(8), 1419–1425 (2017). https://doi.org/10.1007/s11760-017-1102-y
Jian, W.L.: Research on Fire Detection Method Based on Video Smoke Motion Detection. Master's thesis, Nanchang Hangkong University (2018)
Fu, T.J., Zheng, C.E., Tian, Y., Qiu, Q.M., Lin, S.J.: Forest fire recognition based on deep convolutional neural network under complex background. Comput. Modernization 3, 52–57 (2016)
Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J M., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In: IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 877–882. IEEE (2016)
Zhang, Q., Xu, J., Xu, L., Guo, H.: Deep convolutional neural networks for forest fire detection. In: 2016 International Forum on Management, Education and Information Technology Application. Atlantis Press (2016)
Xiang, X.B.: Research on smoke detection algorithm based on video. Zhejiang University, Hangzhou (2017)
Xiao, X., Kong, F.Z., Liu, J.H.: Dynamic and static feature based surveillance video fire detection algorithm. Comput. Sci. 46(z1), 284–286 (2019)
Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In: 2004 International Conference on Image Processing, 2004. ICIP 2004, vol. 3, pp. 1707–1710. IEEE (2004)
Chen, J., He, Y., Wang, J.: Multi-feature fusion based fast video flame detection. Build. Environ. 45(5), 1113–1122 (2010)
Celik, T., Demirel, H., Ozkaramanli, H.: Automatic fire detection in video sequences. In: 2006 14th European Signal Processing Conference, pp. 1–5. IEEE (2006)
Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009)
Chi, R., Lu, Z.M., Ji, Q.G.: Real-time multi-feature based fire flame detection in video. IET Image Proc. 11(1), 31–37 (2016)
Yan, Y.Y., Zhu, X.Y., Liu, Y., Gao, S.B.: Flame detection based on the faster R-CNN model. J. Nanjing Normal Univ. (Nat. Sci. Ed.) 2018(03), 1–5 (2018)
Aslan, S., Güdükbay, U., Töreyin, B.U., Çetin, A.E.: Deep convolutional generative adversarial networks based flame detection in video. arXiv preprint arXiv:1902.01824 (2019)
Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.: Higher order linear dynamical systems for smoke detection in video surveillance applications. IEEE Trans. Circ. Syst. Video Technol. 27(5), 1143–1154 (2016)
Appana, D.K., Islam, R., Khan, S.A., Kim, J.M.: A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems. Inf. Sci. 418, 91–101 (2017)
Barmpoutis, P., Dimitropoulos, K., Grammalidis, N.: Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition. In: 2014 22nd European Signal Processing Conference (EUSIPCO), pp. 1078–1082. IEEE (2014)
Wen, Z.B., Kang, Y., Cao, Y., Wei, M., Song, W.G.: Video smoke detection based on random forest feature selection. J. Univ. Sci. Technol. China 47(8), 653–664 (2017)
Wang, S., He, Y., Yang, H., Wang, K., Wang, J.: Video smoke detection using shape, color and dynamic features. J. Intell. Fuzzy Syst. 33(1), 305–313 (2017)
Luo, Y., Zhao, L., Liu, P., Huang, D.: Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed. Tools Appl. 77(12), 15075–15092 (2017). https://doi.org/10.1007/s11042-017-5090-2
Mao, W., Wang, W., Dou, Z., Li, Y.: Fire recognition based on multi-channel convolutional neural network. Fire Technol. 54(2), 531–554 (2018)
Pan, Y., Yao, T., Li, Y., Mei, T.: X-Linear attention networks for image captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10971–10980 (2020)
Chen, R., Zeng, G., Wang, K., Luo, L., Cai, Z.: A real time vision-based smoking detection framework on edge. J. Internet Things 2(2), 55–64 (2020)
Zhou, S., Chen, L., Sugumaran, V.: Hidden two-stream collaborative learning network for action recognition. Comput. Mater. Continua 63(3), 1545–1561 (2020)
Hu, B., Wang, J.: Deep learning for distinguishing computer generated images and natural images: a survey. J. Inf. Hiding Priv. Prot. 2(2), 37–47 (2020)
Zhou, S., Wu, J., Zhang, F., Sehdev, P.: Depth occlusion perception feature analysis for person re-identification. Pattern Recogn. Lett. 138, 617–623 (2020)
Xiang, L., Guo, G., Li, Q., Zhu, C., Chen, J.: Spam detection in reviews using LSTM-based multi-entity temporal features. Intell. Autom. Soft Comput. 26(6), 1375–1390 (2020)
Acknowledgement
This work was supported by the science and technology program of CSG Power Generation Co., LTD.(Research and Application of Intelligent Perception Technology in Power Plant Production Area Based on Machine Vision 020000KK52190017).
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Zhang, H., Wang, Z., Chen, M., Peng, Y., Gao, Y., Zhou, J. (2021). An Improved YOLOv3 Algorithm Combined with Attention Mechanism for Flame and Smoke Detection. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_20
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