A Comprehensive Guide to Wildfire Prevention with YOLOv8

Welcome to the Wildfire Detection Research project repository! This repository hosts the code and resources related to our research on leveraging computer vision for fire detection. Our aim is to contribute to wildfire prevention efforts by developing and training an object detection model to accurately identify instances of fire and smoke in images.

Highlights:

• Trained YOLOv8 model using the D-Fire dataset for accurate fire and smoke detection.
• Explored different model sizes and their performance metrics.
• Developed a Streamlit app for practical demonstration of the model's capabilities.
• Investigated future implications of using computer vision in wildfire prevention strategies.

For a comprehensive understanding of our research journey, methodology, and results, please refer to our Medium article: Stay Ahead of the Flames: A Comprehensive Guide to Wildfire Prevention with YOLOv8

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Dataset

We utilized the D-Fire dataset, a curated collection of 21,000 labeled images, each annotated in YOLO format. The dataset focuses on fire and smoke instances, while also encompassing diverse visual cues, including non-fire images that resemble fire-like patterns.

D-Fire dataset examples

We have explored many different datasets. Here is the summary:

Dataset	Image type	Camera	# images	Classes	Bounding Boxes
Wildfire Detection Image Data	RGB	Regular	1,875 images	- fire - no fire	no
Fire Detection Using Surveillance Camera on Roads	RGB	Surveillance Camera	10,000 images	- fire - no fire	no
FIRESENSE (Videos)	RGB	Regular, Surveillance Camera	11 fire videos 16 non-fire videos 13 smoke videos 13 non-smoke videos	- fire - no fire - smoke - no smoke	no
Aerial Rescue Object Detection	RGB	Regular, drone	29,810 images	- human - fire - vehicle	yes
Fire detection dataset	RGB	Regular, Surveillance Camera	3,894 images	- fire - no fire	no
Forest Fire	RGB, Gray Scale	Regular, Drone, Surveillance Camera	15,800 images	- fire - smoke	no
Forest Fire Images	RGB	Regular	5,000 images	- fire - no fire	no
Fire Detection in YOLO format	RGB	Regular	500 images	- fire	yes
FLAME 2: FIRE DETECTION AND MODELING: AERIAL MULTI-SPECTRAL IMAGE DATASET	RGB, IR	Drone	53,451 RGB 53,451 IR	- fire and smoke - fire and no smoke - no fire and smoke - no fire and no smoke	no
Forest Fire Dataset	RGB	Regular	1,900 images	- fire - no fire	no
Open Wildfire Smoke Datasets	RGB	Surveillance Camera	2,192 images	- smoke	yes
AIDER: Aerial Image Database for Emergency Response applications	RGB	Aerial view, regular	500 images for each disaster class 4,000 images for the normal class	- Fire/Smoke - Flood - Collapsed Building/Rubble - Traffic Accidents - Normal case	no
Furg Fire Dataset	RGB	Regular	21 videos	- fire	yes
Mivia Fire Detection	RGB	Regular	14 fire videos 17 non-fire videos	- fire - no fire	no
FireNet	RGB	Regular	500 images	- fire	yes
FIRE Dataset	RGB	Regular	755 outdoor-fire images  244 nature images	- fire - no fire	no
Fire Detection v2	RGB	Regular	600 images	- scale1fire - scale2fire - scale3fire	yes
fireDetection Computer Vision Project	RGB	Regular	9,681 images	- Fire - fire	yes
D-Fire	RGB	Regular, Aerial, Surveillance Camera	21,000 images	- Fire - Smoke	yes
Fire-Smoke-Dataset	RGB	Regular	3,000 images	- Fire - Smoke - Neutral	no

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Model Training

We trained the YOLOv8 model by Ultralytics on the D-Fire dataset to achieve accurate fire and smoke detection. Our research not only focuses on achieving high accuracy but also on optimizing model parameters and hyperparameters to ensure efficiency and speed.

Training recipe can be found in a training-recipes folder

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Results

The trained YOLOv8 model demonstrated impressive performance on the D-Fire test dataset, with mAP@50 scores and inference time across different model sizes as follows. Evaluation was done using NVIDIA A100-SXM4-40. Resolution of input images was 640x640.

Model Size	mAP@50	Inference (ms)
Nano	0.787	0.422
Small	0.798	0.773
Medium	0.801	1.532
Large	0.812	2.342
Extra Large	0.814	3.465

For a detailed exploration of our training process and insights, we invite you to read our comprehensive guide on Medium: Stay Ahead of the Flames: A Comprehensive Guide to Wildfire Prevention with YOLOv8

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Future Implications

This research underscores the potential of computer vision in addressing real-world challenges, such as wildfire detection. As technology evolves, integrating machine learning tools into wildfire prevention and emergency response strategies could significantly enhance our ability to detect and mitigate wildfires effectively.

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Streamlit App

For a practical demonstration of our research, you can interact with our Wildfire Detection App, powered by the YOLOv8 model. This app allows you to upload images and observe the model's detection capabilities in action.

To experience the app, visit: Wildfire Detection App

Wildfire Detection App

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Disclaimer

Please note that while our Streamlit app demonstrates the capabilities of our model, it is intended for demonstration purposes and may not be suitable for critical wildfire detection applications.

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Acknowledgment

I would like to acknowledge the Institute of Smart Systems and Artificial Intelligence (ISSAI) at Nazarbayev University for fostering an environment of innovation and research excellence. The support I received from ISSAI has been integral to the successful completion of this endeavor.

I extend my heartfelt appreciation to my supervisor, Askat Kuzdeuov, at ISSAI, whose guidance and mentorship were indispensable to the success of this research. His expertise and support have been invaluable in shaping the direction and quality of this work.

I would also like to extend my thanks to the creators of the D-Fire dataset for providing a valuable resource that underpins the foundation of this research. Additionally, the Ultralytics team's contribution to the YOLOv8 model has been instrumental in enabling accurate and efficient fire detection.

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License

This project is licensed under the MIT License.