Paper Info
Title
Fourier Domain Pruning Of Mobilenet-V2 With Application To Video Based Wildfire Detection
Abstract
In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and prune system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9412613
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Keywords
DocType
ISSN
wildfire detection, block-based division, transfer learning, Fourier analysis, pruning and slimming
Conference
1051-4651
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Hongyi Pan162.58
Diaa Badawi263.93
Ahmet Enis Çetin331.76