The structure of FasterNext.

The structure of FasterNext.

<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints...

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Bibliographic Details
Main Author: Junjie Lu (160350) (author)
Other Authors: Yuchen Zheng (4172806) (author), Liwei Guan (21514868) (author), Bing Lin (136655) (author), Wenzao Shi (22008619) (author), Junyan Zhang (532480) (author), Yunping Wu (9472035) (author)
Published: 2025
Subjects:
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
time fire detection
small fire targets
reduce resource consumption
reduce computational cost
limited computational resources
improve detection performance
hardware resource constraints
enhance detection precision
diou loss function
yolo increases map
paper proposes fcmi
efficient deep learning
div >< p
deep learning
significantly exacerbating
rapid development
practical value
neck network
lowers gflops
lightweight design
inference speed
fasternext module
experimental results
edge scenarios
edge devices
based algorithm
40 %,
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