Deep Learning based method for Fire Detection

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2023-10-09
Department
Major/Subject
Machine Learning
Mcode
SCI3113
Degree programme
Master’s Programme in Security and Cloud Computing (SECCLO)
Language
en
Pages
59
Series
Abstract
Fire accidents have become increasingly frequent and have profound effects on today’s society, leading to injuries, fatalities, and significant economic losses. It is crucial to develop effective and early fire detection systems that can promptly detect and prevent fire disasters. Machine learning and computer vision provide a promising solution for the early detection of fires, mitigating potential risks and enhancing safety measures. In this study, we present an extensive and comprehensive fire dataset, surpassing existing datasets in terms of both scale and diversity. This dataset enables robust and thorough training of fire detection models and serves as a benchmark for evaluating future fire detection systems. The core of our fire detection system is the state-of-the-art Yolov5 model, known for its simplicity, speed, and efficiency in object detection tasks. We demonstrate the effectiveness of our proposed model with promising results, achieving an average F1 score of 0.77 and an mAP@0.5 score of approximately 0.77. These metrics reflect the model’s capability to accurately detect fires across various scenarios. Moreover, we take our research further by focusing on the deployment of the trained model to the cloud. The cloud deployment aspect enhances the practicality and accessibility of our fire detection system, making it more scalable and efficient. Furthermore, it opens up avenues for future advancements and integration with other smart technologies, contributing to the development of smarter and safer environments. Overall, this work contributes to the advancement of fire detection systems, offering a robust dataset, a powerful detection model, and an efficient cloud deployment approach. With this research, we aim to foster a safer and more secure environment by reducing the risks posed by fire accidents and enabling timely and effective fire prevention measures.
Description
Supervisor
Jung, Alexander
Thesis advisor
Khajavi, Siavash
Keywords
fire detection, yolo, cloud, machine learning
Other note
Citation