Home

Awesome

FireNet

FireNet is an artificial intelligence project for real-time fire detection. <br><br> <img src="images/fire_net.jpg" />

<hr> <b>FireNet</b> is a real-time fire detection project containing an annotated dataset, pre-trained models and inference codes, all created to ensure that machine learning systems can be trained to detect fires instantly and eliminate false alerts. This is part of <a href="https://deepquestai.com" >DeepQuest AI</a>'s to train machine learning systems to perceive, understand and act accordingly in solving problems in any environment they are deployed. <br><br>

This is the first release of the FireNet. It contains an annotated dataset of 502 images splitted into 412 images for training and 90 images for validation.

<br>

<b>>>> DOWNLOAD, TRAINING AND DETECTION: </b> <br><br> The <b>FireNet</b> dataset is provided for download in the <b>release</b> section of this repository. You can download the dataset via the link below.<br><br> <a href="https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/fire-dataset.zip" >https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/fire-dataset.zip</a> <br><br>

We have also provided a ImageAI codebase to train a <b>YOLOv3</b> detection model on the images and perform detection in mages and videos using a pre-trained model (also using <b>YOLOv3</b>) provided in the release section of this repository. The python codebase is contained in the <b><a href="fire_net.py" >fire_net.py</a></b> file and the detection configuration JSON file for detection is also provided the <b><a href="detection_config.json" >detection_config.json</a></b>. The pretrained <b>YOLOv3</b> model is available for download via the link below. <br><br> <b><a href="https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/detection_model-ex-33--loss-4.97.h5" >https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/detection_model-ex-33--loss-4.97.h5</a></b><br> <br> <br> Running the experiment or detection requires that you have Tensorflow, and Keras, OpenCV and ImageAI installed. You can install this dependencies via the commands below.

<br><span><b>- Tensorflow 1.4.0 (and later versions) </b> <a href="https://www.tensorflow.org/install/install_windows" style="text-decoration: none;" > Install</a></span> or install via pip <pre> pip3 install --upgrade tensorflow </pre>

<span><b>- OpenCV </b> <a href="https://pypi.python.org/pypi/opencv-python" style="text-decoration: none;" >Install</a></span> or install via pip <pre> pip3 install opencv-python </pre>

<span><b>- Keras 2.x </b> <a href="https://keras.io/#installation" style="text-decoration: none;" >Install</a></span> or install via pip <pre> pip3 install keras </pre>

<span><b>- ImageAI 2.0.3 </b>
<span> <pre>pip3 install imageai --upgrade </pre></span> <br><br> <br>

<b>>>> Video & Prediction Results</b> <br><br> Click below to watch the video demonstration of the trained model at work. <br> <a href="https://www.youtube.com/watch?v=ts3yxfNrDnY" ><img src="images/video.jpg" /></a> <br><br><br><br> <img src="images/1-detected.jpg" />

<br> <img src="images/2-detected.jpg" />

<br> <br>

<img src="images/3-detected.jpg" style="width: 650px;" /> <br> <br> <h3><b><u>References</u></b></h3>
  1. Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement <br> <a href="https://arxiv.org/abs/1804.02767" >https://arxiv.org/abs/1804.02767</a> <br><br>