SqueezeSlimU-Net: An Adaptive and Efficient Segmentation Architecture for Real-Time UAV Weed Detection

SqueezeSlimU-Net: An Adaptive and Efficient Segmentation Architecture for Real-Time UAV Weed Detection

The limited processing capacity of computing equipment that is usually mounted on unmanned aerial vehicles (UAVs) often prevents real-time execution of computer vision tasks, such as image segmentation. In this article, we introduce SqueezeSlimU-Net (SSU-Net), an adaptive and efficient deep learning...

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Bibliographic Details
Main Authors: Alina L. Machidon, Andraz Krasovec, Veljko Pejovic, Octavian M. Machidon
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Adaptive neural networks
computational efficiency
image segmentation
precision agriculture
real-time unmanned aerial vehicle (UAV) vision
weed detection
Online Access:https://ieeexplore.ieee.org/document/10857312/
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https://ieeexplore.ieee.org/document/10857312/

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