Advertisement

Deep CNN-ELM Hybrid Models for Fire Detection in Images

  • Jivitesh Sharma
  • Ole-Christopher Granmo
  • Morten Goodwin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)

Abstract

In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNNs: VGG16 and Resnet50 and replace the softmax classifier with the ELM classifier. For both the VGG16 and Resnet50, the number of fully connected layers is also reduced. Especially in VGG16, which has 3 fully connected layers of 4096 neurons each followed by a softmax classifier, we replace two of these with an ELM classifier. The difference in convergence rate between fine-tuning the fully connected layers of pre-trained models and training an ELM classifier are enormous, around 20x to 51x speed-up. Also, we show that using an ELM classifier increases the accuracy of the system by 2.8% to 7.1% depending on the CNN feature extractor. We also compare our hybrid architecture with another hybrid architecture, i.e. the CNN-SVM model. Using SVM as the classifier does improve accuracy compared to state-of-the-art deep CNNs. But our Deep CNN-ELM model is able to outperform the Deep CNN-SVM models. (Preliminary version of some of the results of this paper appear in “Deep Convolutional Neural Networks for Fire Detection in Images”, Springer Proceedings Engineering Applications of Neural Networks 2017 (EANN’17), Athens, Greece, 25–27 August).

Keywords

Deep convolutional neural networks Extreme learning machine Image classification Fire detection 

References

  1. 1.
    Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: From generic to specific deep representations for visual recognition. CoRR, abs/1406.5774 (2014)Google Scholar
  2. 2.
    Bradski, G.: OpenCV. Dr. Dobb’s J. Soft. Tools 25, 120–126 (2000)Google Scholar
  3. 3.
    Cao, L., Huang, W., Sun, F.: A deep and stable extreme learning approach for classification and regression. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 1. PALO, vol. 3, pp. 141–150. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14063-6_13CrossRefGoogle Scholar
  4. 4.
    Chino, D.Y.T., Avalhais, L.P.S., Rodrigues Jr., J.F., Traina, A.J.M.: BoWFire: detection of fire in still images by integrating pixel color and texture analysis. CoRR, abs/1506.03495 (2015)Google Scholar
  5. 5.
    Chollet, F.: Keras (2015)Google Scholar
  6. 6.
    Zhao, J., et al.: Image based forest fire detection using dynamic characteristics with artificial neural networks. In: 2009 International Joint Conference on Artificial Intelligence, pp. 290–293, April 2009Google Scholar
  7. 7.
    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, October 2016Google Scholar
  8. 8.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)CrossRefGoogle Scholar
  9. 9.
    Gürpinar, F., Kaya, H., Dibeklioglu, H., Salah, A.A.: Kernel ELM and CNN based facial age estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 785–791, June 2016Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  11. 11.
    Horng, W.-B., Peng, J.-W.: Image-based fire detection using neural networks. In: JCIS (2006)Google Scholar
  12. 12.
    Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)CrossRefGoogle Scholar
  13. 13.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks, Proceedings, vol. 2, pp. 985–990. IEEE (2004)Google Scholar
  14. 14.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)CrossRefGoogle Scholar
  15. 15.
    Kim, J., Kim, J., Jang, G.-J., Lee, M.: Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw. 87, 109–121 (2017)CrossRefGoogle Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)Google Scholar
  17. 17.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc (2012)Google Scholar
  18. 18.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  19. 19.
    McDonnell, M.D., Tissera, M.D., van Schaik, A., Tapson, J.: Fast, simple and accurate handwritten digit classification using extreme learning machines with shaped input-weights. CoRR, abs/1412.8307 (2014)Google Scholar
  20. 20.
    McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. CoRR, abs/1503.04596 (2015)Google Scholar
  21. 21.
    Nagi, J., Di Caro, G.A., Giusti, A., Nagi, F., Gambardella, L.M.: Convolutional neural support vector machines: hybrid visual pattern classifiers for multi-robot systems. In: ICMLA, no. 1, pp. 27–32. IEEE (2012)Google Scholar
  22. 22.
    Pang, S., Yang, X.: Deep convolutional extreme learning machine and its application in handwritten digit classification. Intell. Neurosci. 2016 (2016)Google Scholar
  23. 23.
    Tomas Polednik, Bc.: Detection of fire in images and video using CNN. Excel@FIT (2015)Google Scholar
  24. 24.
    Poobalan, K., Liew, S.C.: Fire detection algorithm using image processing techniques. In: 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), Ocotober 2015Google Scholar
  25. 25.
    Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. CoRR, abs/1403.6382 (2014)Google Scholar
  26. 26.
    Ribeiro, B., Lopes, N.: Extreme learning classifier with deep concepts. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8258, pp. 182–189. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41822-8_23CrossRefGoogle Scholar
  27. 27.
    Custer, R.B.R.: Fire detection: the state of the art. NBS Technical Note, US Department of Commerce (1974)Google Scholar
  28. 28.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Yu, J.S., Chen, J., Xiang, Z.Q., Zou, Y.X.: A hybrid convolutional neural networks with extreme learning machine for WCE image classification. In: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1822–1827, December 2015Google Scholar
  30. 30.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229 (2013)Google Scholar
  31. 31.
    Shao, J., Wang, G., Guo, W.: An image-based fire detection method using color analysis. In: 2012 International Conference on Computer Science and Information Processing (CSIP), pp. 1008–1011, August 2012Google Scholar
  32. 32.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar
  33. 33.
    Tang, Y.: Deep learning using support vector machines. CoRR, abs/1306.0239 (2013)Google Scholar
  34. 34.
    Tao, C., Zhang, J., Wang, P.: Smoke detection based on deep convolutional neural networks. In: 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), pp. 150–153, December 2016Google Scholar
  35. 35.
    Tapson, J., de Chazal, P., van Schaik, A.: Explicit computation of input weights in extreme learning machines. CoRR, abs/1406.2889 (2014)Google Scholar
  36. 36.
    Toulouse, T., Rossi, L., Celik, T., Akhloufi, M.: Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods. Sig. Image Video Process. 10(4), 647–654 (2016)CrossRefGoogle Scholar
  37. 37.
    Töreyin, B.U., Dedeoǧlu, Y., Güdükbay, U., Çetin, A.E.: Computer vision based method for real-time fire and flame detection. Patt. Recogn. Lett. 27(1), 49–58 (2006)CrossRefGoogle Scholar
  38. 38.
    Wolfshaar, J.V.D., Karaaba, M.F., Wiering, M.A.: Deep convolutional neural networks and support vector machines for gender recognition. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 188–195, December 2015Google Scholar
  39. 39.
    Verstockt, S., Lambert, P., Van de Walle, R., Merci, B., Sette, B.L State of the art in vision-based fire and smoke dectection. In: Luck, H., Willms, I. (eds.) 14th International Conference on Automatic Fire Detection, Proceedings, vol. 2, pp. 285–292. University of Duisburg-Essen. Department of Communication Systems (2009)Google Scholar
  40. 40.
    Vicente, J., Guillemant, P.: An image processing technique for automatically detecting forest fire. Int. J. Therm. Sci. 41(12), 1113–1120 (2002)CrossRefGoogle Scholar
  41. 41.
    Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using deep convolutional networks and extreme learning machine. In: He, X., et al. (eds.) IScIDE 2015. LNCS, vol. 9242, pp. 272–280. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23989-7_28Google Scholar
  42. 42.
    Zhang, L., Zhang, D.: SVM and ELM: who wins? object recognition with deep convolutional features from imagenet. CoRR, abs/1506.02509 (2015)Google Scholar
  43. 43.
    Zhang, Q., Xu, J., Xu, L., Guo, H.: Deep convolutional neural networks for forest fire detection, February 2016Google Scholar
  44. 44.
    Zhu, W., Miao, J., Qing, L.: Constrained extreme learning machine: a novel highly discriminative random feedforward neural network. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 800–807, July 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jivitesh Sharma
    • 1
  • Ole-Christopher Granmo
    • 1
  • Morten Goodwin
    • 1
  1. 1.Center for Artificial Intelligence ResearchUniversity of AgderGrimstadNorway

Personalised recommendations