forest - fire smoke response system deep learning - based approaches for analysis of cctv images
Harina J M,Janani R,Leelavathi N V
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 23 April,2025 Pages:2036-2042
Abstract
An effective forest-fire response is critical for minimizing the losses caused by forest fires. The purpose of this study is to construct a model for early fire detection in video and live feed response systems based on deep learning. Initially, we implement neural architecture search-based detection using the YOLO (You Only Look Once) model. Backbone networks play a crucial role in the application of deep learningbased models, as they have a significant impact on the performance of the model. To train and test our fire detection models, we utilize a large-scale fire dataset. We then compare the searched lightweight backbone with well-known backbones, such as YOLO4 Model and inception model. Additionally, our system captures essential metadata, including the date, time, latitude, and longitude of the fire detection area. This geographic information can be invaluable for the immediate repeated response and the coordination during firefighting efforts. Once a fire is detected, the system seamlessly transfers the collected data to an alert database, enabling immediate response actions.
Kewords
Private key, public key, CBI key and encrypting the data storage
Reference
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