Forest Fire Detection using Convolutional Neural Networks (CNN) | Python Deep Learning Project 2023

Forest Fire Detection using Convolutional Neural Networks (CNN) | Python Deep Learning IEEE Final Year Project 2023.
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📌IEEE Base Paper Title: Forest Fire Detection using Convolutional Neural Networks (CNN).
🔬Our Proposed Project Title: FireWatch: Deep Learning-based Fire and Smoke Detection System with MobileNet Architecture.
💡Implementation Code: PYTHON.
🔬Algorithm / Model Used: MobileNet Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

IEEE Base paper Abstract:
One of the most significant and essential resources is the forest because it features a variety of plant life, including herbs, trees, and bushes, as well as several animal species. These renewable resources are crucial to humanity in some way. Forest fires, the most common hazard to forests, severely devastate the ecology, and local ecosystem. To preserve forests from fires, early detection and preventive measures are required. The two most common existing approaches for human surveillance to accomplish early detection are Direct human monitoring and remote video surveillance. This study proposes a forest fire image identification approach using convolutional neural networks to detect fires automatically. Employing this technique decreases false alarms and provides accurate fire detection results. The contour approach can be used to test its capability to monitor both interior and outdoor applications utilizing computer vision.

REFERENCE:
D Ranjani; M Haripriyabala; J Indhu; V Janani; A Jothi, “Forest Fire Detection using Convolutional Neural Networks (CNN)”, 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE Conference, 2023.

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