Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection - supporting materials

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the nontemporal real-time bounds detection of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architectures (NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental analysis to optimise the computational efficiency for this task. The results improve upon the current state-of-the-art solution for fire detection, achieving an accuracy of 95% for full-frame binary classification and 97% for superpixel localisation. We notably achieve a classification speed up by a factor of 2.3× for binary classification and 1.3× for superpixel localisation, with runtime of 40 fps and 18 fps respectively, outperforming prior work in the field presenting an efficient, robust and real-time solution for fire region detection. Subsequent implementation on low-powered devices (Nvidia Xavier-NX, achieving 49 fps for full-frame classification via ShuffleNetV2-OnFire) demonstrates our architectures are suitable for various real-world deployment applications. | Cited in: Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (W. Thomson, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2020. | This collection contains supporting materials in the form of the pre-trained network models and image data used in the study.