Fire Detection Using Keras
AP Research Project (2022-23)
For my AP Research class, I tried to answer the following: "How can machine learning best be utilized to accurately classify video footage of forest fires on the average laptop computer?" In order to do so, I developed and implemented a convolutional neural network (CNN) in Google Colab using the Keras library. I also made use of methods in other libraries such as TensorFlow, NumPy, OpenCV, and matplotlib. In the collection of images below, I have included figures from my paper.
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The CNN employed a binary classification method in order to identify images from either a fire or non-fire class, and I ended up training four different models in order to test optimal performance. In total, the final model trained on 3,760 images taken from Kaggle and other open source datasets. The highest F1 scores achieved was 0.96 for both the fire and non-fire classes. See the fire_detection GitHub repository for more information.
TOOLS
GALLERY
Fig. 1
CNN layer implementation
Fig. 2
Video analysis implementation
Fig. 3
Comparing the four common fire detection methods
Fig. 4
True positive, true negative, false positive, and false negatives in the project
Fig. 5
Comparing max pooling and average pooling
Fig. 6
Visualization of batch normalization
Fig. 7
Multiple CNN visualization
Fig. 8
Comparing traditional and transfer learning
Fig. 9
Convolution layer visualization
Fig. 10
Padding visualization
Fig. 11
Dropout visualization
Fig. 12
Normalization visualization
