Implementation of Lightweight Convolutional Neural Networks with an Early Exit Mechanism Utilizing 40 nm CMOS Process for Fire Detection in Unmanned Aerial Vehicles

Implementation of Lightweight Convolutional Neural Networks with an Early Exit Mechanism Utilizing 40 nm CMOS Process for Fire Detection in Unmanned Aerial Vehicles

The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational...

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Bibliographic Details
Main Authors: Yu-Pei Liang, Chen-Ming Chang, Ching-Che Chung
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Sensors
Subjects:
unmanned aerial vehicles (UAVs)
fire detection
neural networks
quantization
fixed-point arithmetic
real-time systems
Online Access:https://www.mdpi.com/1424-8220/24/7/2265

Internet

https://www.mdpi.com/1424-8220/24/7/2265

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