Performance comparison of YOLOv5s with FasterNext module replacement.

Performance comparison of YOLOv5s with FasterNext module replacement.

<p>Performance comparison of YOLOv5s with FasterNext module replacement.</p>

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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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Summary:	<p>Performance comparison of YOLOv5s with FasterNext module replacement.</p>

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