This project introduces a sophisticated image processing algorithm designed for real-time fire detection, specifically targeting wildfire occurrences in forest areas [1,2]. Developed using MATLAB Simulink, the system not only highlights the potential of image processing in critical environmental applications but also demonstrates a scalable solution from static image analysis to dynamic, real-time detection via webcams. Ultimately, the algorithm's deployment on a Raspberry Pi board equipped with a camera module showcases a practical, field-deployable system aimed at minimizing wildfire impact by enabling early detection and response efforts.

Wildfires pose a significant threat to forests worldwide, leading to substantial ecological, economic, and social consequences. Early detection is crucial in mitigating these impacts, offering a crucial window for firefighting efforts to commence before the fire escalates beyond control. This project addresses this critical need by providing an efficient, real-time detection system capable of operating in diverse environmental conditions, thereby contributing to the preservation of forest resources and wildlife habitats.

• Simulink Development: Utilizes MATLAB Simulink for the development and testing of the fire detection algorithm, ensuring a high degree of accuracy and reliability.
• Versatile Testing Methods: Employs both static images and videos for initial testing, fine-tuning the system for optimal performance.
• Real-Time Detection: Integrates webcam functionality for live monitoring, crucial for the dynamic nature of fire spread in forest environments.
• Portable Deployment: Implements the final solution on a Raspberry Pi board with a camera, offering a compact, deployable system for field application.

• MATLAB & Simulink for algorithm development and testing
• Raspberry Pi for practical deployment
• Raspberry Pi Camera Module for real-time video capture

This section will guide users through setting up the development environment, installing necessary software and libraries, and steps to execute the project.

Below are result images demonstrating the algorithm's capability to accurately detect fires in various conditions, underlining the system's potential impact in forest fire management and prevention.

[1] C. E. Premal and S. S. Vinsley, "Image processing based forest fire detection using YCbCr colour model," 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], Nagercoil, India, 2014, pp. 1229-1237, doi: 10.1109/ICCPCT.2014.7054883.

[2] He, Bingsong & Zhao, Xueping & Zhou, Zhiguo & Fan, Zheyi. (2013). Implementation of a Fire Detection Algorithm on TMS320DM642 DSP using MATLAB/Simulink. 10.2991/iccnce.2013.155.