Early Wildfire Smoke Detection Based on Motion-based Geometric Image Transformation and Deep Convolutional Generative Adversarial Networks

Abstract

Early detection of wildfire smoke in real-time is essentially important in forest surveillance and monitoring systems. We propose a vision-based method to detect smoke using Deep Convolutional Generative Adversarial Neural Networks (DC-GANs). Many existing supervised learning approaches using convolutional neural networks require substantial amount of labeled data. In order to have a robust representation of sequences with and without smoke, we propose a two-stage training of a DCGAN. Our training framework includes, the regular training of a DCGAN with real images and noise vectors, and training the discriminator separately using the smoke images without the generator. Before training the networks, the temporal evolution of smoke is also integrated with a motion-based transformation of images as a pre-processing step. Experimental results show that the proposed method effectively detects the smoke images with negligible false positive rates in real-time.

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

“…Then, a GAN was employed for predicting the smoke trend heatmap based on the space–time analysis of the smoke videos. Finally, in [ 69 ] the authors used a two-stage training of deep convolutional GANs for smoke detection. This procedure included a regular training step of a deep convolutional (DC)-GAN with real images and noise vectors and a training step of the discriminator separately using the smoke images without the generator.…”

Additive neural network for forest fire detection

“…AddNet can be also applied to three-dimensional wildfire detection schemes such as [6]. Moreover, a 3D extension of the AddNet can be also employed to process videos in time domain like regular CNN does [26], and the gain with the AddNet respect to the regular 3D CNN would be higher in terms of computational time.…”

“…Early detection of wildfire is critical to minimizing environmental and human losses. In recent years, there has been significant interest in developing real-time algorithms to detect wildfires using regular video-based surveillance systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Video-based forest fire detection can be used to replace traditional point-sensortype detectors since a single camera can monitor a very large area from a distance and can detect wildfire smoke immediately after fire eruption as long as the smoke is within the viewing range of the camera.…”

Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

“…This method replaces traditional convolutional layers with normalisation and convolutional layers to accelerate the training process and boost the performance of smoke detection. Aslan et al [ 25 ] proposed a two-stage training method for deep convolutional generative adversarial neural networks (DC-GANs). This method trains the DC-GANs with real images and noise vectors, and the discriminator is separately trained using the smoke images without a generator.…”