Deep Learning-Based Fire and Smoke Detection ...

Deep Learning-Based Fire and Smoke Detection System with MobileNet Architecture – A Review

Author Name : Prof. Shruti Kolte, Mr. Anurag Mahakalkar, Mr. Shreyash Arghode , Mr. Geyesh Barsagade , Mr. Abhishek Kongare

ABSTRACT One of the most frequent yet undesirable phenomena brought on by climate change and rising temperatures is wildfires or any other areas. 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. 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.