Fire Detection
11 papers with code • 1 benchmarks • 1 datasets
Detection of fire using multi-variate time series sensor data.
Benchmarks
These leaderboards are used to track progress in Fire Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
895 Fire Videos Data
Most implemented papers
FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
arpit-jadon/FireNet-LightWeight-Network-for-Fire-Detection
•
•
Fire disasters typically result in lot of loss to life and property.
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
NeelBhowmik/efficient-compact-fire-detection-cnn
•
•
Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat).
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method.
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System
herminarto/DLCV
•
•
Jurnal Ilmu Komputer dan Informasi 2020
This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images.
Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset
FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies.
Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study
Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement.
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings.
Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
JorgeFCS/Deep-Learning-fire-segmentation
•
•
Applied Sciences 2021
However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results.
ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation
In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas.
Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery
Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region.
