Minimal inference scripts for ....

Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection

Tested using Python 3.4.6, TensorFlow 1.13.0, tflearn 0.3 and OpenCV 3.3.1 / 4.0.x

Abstract:

"In this work we investigate the automatic detection of fire pixel regions in video (or still) imagery within real-time bounds without reliance on temporal scene information. As an extension to prior work in the field, we consider the performance of experimentally defined, reduced complexity deep convolutional neural network (CNN) architectures for this task. Contrary to contemporary trends in the field, our work illustrates maximal accuracy of 0.93 for whole image binary fire detection (1), with 0.89 accuracy within our superpixel localization framework can be achieved (2), via a network architecture of significantly reduced complexity. These reduced architectures additionally offer a 3-4 fold increase in computational performance offering up to 17 fps processing on contemporary hardware independent of temporal information (1). We show the relative performance achieved against prior work using benchmark datasets to illustrate maximally robust real-time fire region detection."

(1) using InceptionV1-OnFire CNN model (2) using SP-InceptionV1-OnFire CNN model

Dunnings and Breckon, In Proc. International Conference on Image Processing IEEE, 2018

Usage

This code requires the models firenet.tflite and FireNet.

1. run download-models.sh to download FireNet
2. run converter/firenet-conversion.py to obtain firenet.tflite

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Reference:

If making use of this work in any way (including our pretrained models or dataset), you must reference the following article in any report, publication, presentation, software release or any other materials:

Experimentally defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection (Dunnings and Breckon), In Proc. International Conference on Image Processing IEEE, 2018.

@InProceedings{dunnings18fire,
  author =     {Dunnings, A. and Breckon, T.P.},
  title =      {Experimentally defined Convolutional Nerual Network Architecture Variants for Non-temporal Real-time Fire Detection},
  booktitle =  {Proc. International Conference on Image Processing},
  pages =      {1558-1562},
  year =       {2018},
  month =      {September},
  publisher =  {IEEE},
  doi = 	 {10.1109/ICIP.2018.8451657},
  keywords =   {simplified CNN, deep learning, fire detection, real-time, non-temporal, non-stationary visual fire detection},
}

In addition the terms of the LICENSE must be adhered to.

Acknowledgements:

Atharva (Art) Deshmukh (Durham University, github and data set collation for publication).

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