Autofocus Vision System Enhancement for UAVs via Autoencoder Generative Algorithm

The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus...

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Published inEngineering, technology & applied science research Vol. 14; no. 6; pp. 18867 - 18872
Main Authors Ahmed, Anwer, Farhan, Rabah Nori
Format Journal Article
LanguageEnglish
Published 02.12.2024
Online AccessGet full text
ISSN2241-4487
1792-8036
1792-8036
DOI10.48084/etasr.8519

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Abstract The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus, and AF has become very important for many fields, possessing advantages such as high efficiency and autonomously interacting with Fenvironmental conditions. The proposed AF vision system for Unmanned Aerial Vehicle (UAV) navigation uses an autoencoder technique to extract important features from images. The system's function is to monitor and control the focus of a camera mounted to a drone. On an AF dataset, the proposed autoencoder model exhibited an amazing 95% F-measure and 90% accuracy, so it can be considered a robust option for achieving precision and clarity in varying conditions since it can effectively identify features.
AbstractList The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus, and AF has become very important for many fields, possessing advantages such as high efficiency and autonomously interacting with Fenvironmental conditions. The proposed AF vision system for Unmanned Aerial Vehicle (UAV) navigation uses an autoencoder technique to extract important features from images. The system's function is to monitor and control the focus of a camera mounted to a drone. On an AF dataset, the proposed autoencoder model exhibited an amazing 95% F-measure and 90% accuracy, so it can be considered a robust option for achieving precision and clarity in varying conditions since it can effectively identify features.
Author Farhan, Rabah Nori
Ahmed, Anwer
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