Abstract
This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. The study contributes to the intersection of artificial intelligence and autonomous systems, aiming to improve early detection capabilities for critical scenarios.
Faculty Advisor/Mentor
Jiang Peng, Cuong Quach
Document Type
Paper
Disciplines
Artificial Intelligence and Robotics | Computer Engineering | Theory and Algorithms
DOI
10.25776/gc6z-6b06
Publication Date
12-1-2023
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Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Theory and Algorithms Commons
Integrating AI into UAVs
This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. The study contributes to the intersection of artificial intelligence and autonomous systems, aiming to improve early detection capabilities for critical scenarios.