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Fire & Smoke Detection using RaspberryPi 5
I developed a fire and smoke detection model using YOLOv8 trained on a large dataset to ensure high accuracy, deployed on a Raspberry Pi for real-time detection
Industry
Industry
Machine Learning
Machine Learning
Hours
Hours
52
52
Skills
Skills
Python, VNC, OpenCV, ML
Python, VNC, OpenCV, ML
Python, VNC, OpenCV, ML
Challenge
The aim of this project was to create a robust fire and smoke detection model capable of real-time, on-device inference, specifically designed for low-cost, resource-limited environments. Early in the project, I identified YOLOv8 as the ideal framework due to its balance of speed and accuracy, but tailoring it for a Raspberry Pi deployment required significant optimization.
I trained the model on over 40,000 images, representing a variety of conditions and environments, using Google Colab for efficient processing. One of the key challenges was handling the extensive data requirements and ensuring the model’s robustness across scenarios, including different lighting, smoke density, and fire size. Additionally, deploying on a Raspberry Pi introduced constraints on processing power, so I had to fine-tune the model’s architecture and parameters to achieve fast, reliable detection while working within the Pi’s limitations.
Results
The final model demonstrates high accuracy and responsiveness in detecting fire and smoke in real time, even in variable conditions. I was pleasantly surprised by how well the model performed on the Raspberry Pi, achieving low-latency inference without compromising detection accuracy. This compact, on-device solution is both accessible and effective, making it ideal for small businesses, homes, and other settings where traditional fire safety systems may be impractical.
This project combined deep learning, computer vision, and embedded system optimization, providing me with hands-on experience in the end-to-end development and deployment of machine learning models for safety applications. The success of this solution reinforces the potential of edge computing for practical, low-cost, and life-saving applications.
Challenge
The aim of this project was to create a robust fire and smoke detection model capable of real-time, on-device inference, specifically designed for low-cost, resource-limited environments. Early in the project, I identified YOLOv8 as the ideal framework due to its balance of speed and accuracy, but tailoring it for a Raspberry Pi deployment required significant optimization.
I trained the model on over 40,000 images, representing a variety of conditions and environments, using Google Colab for efficient processing. One of the key challenges was handling the extensive data requirements and ensuring the model’s robustness across scenarios, including different lighting, smoke density, and fire size. Additionally, deploying on a Raspberry Pi introduced constraints on processing power, so I had to fine-tune the model’s architecture and parameters to achieve fast, reliable detection while working within the Pi’s limitations.
Results
The final model demonstrates high accuracy and responsiveness in detecting fire and smoke in real time, even in variable conditions. I was pleasantly surprised by how well the model performed on the Raspberry Pi, achieving low-latency inference without compromising detection accuracy. This compact, on-device solution is both accessible and effective, making it ideal for small businesses, homes, and other settings where traditional fire safety systems may be impractical.
This project combined deep learning, computer vision, and embedded system optimization, providing me with hands-on experience in the end-to-end development and deployment of machine learning models for safety applications. The success of this solution reinforces the potential of edge computing for practical, low-cost, and life-saving applications.
