Deep Convolutional Generative Adversarial Networks for Flame Detection in Video

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Aslan S., Güdükbay U., Töreyin B. U., Çetin A. E.

12th International Conference on Computational Collective Intelligence, ICCCI 2020, Dha-Nang, Vietnam, 30 November - 03 December 2020, pp.807-815 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1007/978-3-030-63007-2_63
  • City: Dha-Nang
  • Country: Vietnam
  • Page Numbers: pp.807-815
  • Istanbul Technical University Affiliated: Yes


© 2020, Springer Nature Switzerland AG.Real-time flame detection is crucial in video-based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require a substantial amount of labeled data. To have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false-positive rates in real-time.