Name
Title | ||
|---|---|---|
Deep Neural Network with Walsh-Hadamard Transform Layer For Ember Detection during a Wildfire |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPRW56347.2022.00040 | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | DocType | Volume |
deep neural network,ember detection method,infrared video,firebrands,WHT layer,high-frequency activity,standard single slice ResNet-18,ResNet-34,Walsh-Hadamard transform layer,wildfire superspreaders,IR video processing | Conference | 2022 |
Issue | ISSN | ISBN |
1 | 2160-7508 | 978-1-6654-8740-5 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyi Pan | 1 | 6 | 2.58 |
| Diaa Badawi | 2 | 6 | 3.93 |
| Chang Chen | 3 | 0 | 0.34 |
| Adam Watts | 4 | 0 | 0.34 |
| Erdem Koyuncu | 5 | 0 | 1.35 |
| A. Enis Çetin | 6 | 871 | 118.56 |