Deep Neural Network With Walsh-Hadamard Transform Layer for Ember Detection During a Wildfire

Hongyi Pan, Diaa Badawi, Chang Chen, Adam Watts, Erdem Koyuncu, Ahmet Enis Cetin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 257-266

Abstract

In this article, we describe an ember detection method in infrared (IR) video. Embers, also called firebrands, can act as wildfire super-spreaders. We develop a novel neural network with a Walsh-Hadamard Transform (WHT) layer to process the IR video. The WHT layer is used to process the temporal dimension of the video data to model the high-frequency activity due to ember movements. We insert the WHT layer to ResNet-18 and obtained higher accuracy compared to the standard single slice ResNet-18 and the ResNet-18 processing the entire video block. We also repeat the experiments on ResNet-34, but we found that ResNet-18 is sufficient for this task. Therefore, we choose the ResNet-18 with the WHT layer as the proposed model.

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[bibtex]
@InProceedings{Pan_2022_CVPR, author = {Pan, Hongyi and Badawi, Diaa and Chen, Chang and Watts, Adam and Koyuncu, Erdem and Cetin, Ahmet Enis}, title = {Deep Neural Network With Walsh-Hadamard Transform Layer for Ember Detection During a Wildfire}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {257-266} }