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
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.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
OUR PROPOSED PROJECT ABSTRACT:
One of the most frequent yet undesirable phenomena brought on by climate change and rising temperatures is wildfires. Due to the regular occurrence of these extremely strong wildfire episodes, flora and animals are suffering significant degradation. Therefore, there is a need for advanced yet user-friendly systems that at the very least enable the effective use of contemporary tools and solutions. Fire and Smoke detection are crucial tasks in ensuring the safety and security of various environments. In this project, we present a comprehensive solution for fire and smoke detection using deep learning techniques. The project is developed in Python, utilizing the powerful capabilities of the MobileNet architecture. The main objective of this project is to accurately identify fire and smoke instances in different scenarios, including images, videos, and real-time webcam feeds. To achieve this, a robust deep learning model is trained on a diverse dataset consisting of 3825 images of fire, smoke, and normal situations. The implemented deep learning model demonstrates impressive performance, achieving a training accuracy of 97.00% and a validation accuracy of 94.00%. The high accuracy indicates the model’s ability to effectively classify fire, smoke, and normal instances, enabling reliable detection in various contexts. The proposed system allows for multi-purpose detection, providing real-time analysis of images, videos, and live webcam feeds. This versatility ensures the applicability of the solution in a wide range of scenarios, such as surveillance systems, fire alarm systems, and emergency response management. Overall, this project contributes to the field of fire and smoke detection by leveraging deep learning techniques and the MobileNet architecture. The developed system offers an efficient and accurate solution for identifying fire and smoke instances in different visual media, thus enhancing safety and security measures in various environments.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 6 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.8
- Web Framework : Flask.
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.