GSConv working principle.

<div><p>In the domain of remote sensing image small target detection, challenges such as difficulties in extracting features of small targets, complex backgrounds that easily lead to confusion with targets, and high computational complexity with significant resource consumption are preva...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Pingping Yan (462509) (author)
مؤلفون آخرون:	Xiangming Qi (14444617) (author), Liang Jiang (146314) (author)
منشور في:	2025
الموضوعات:	Biochemistry Medicine Molecular Biology Neuroscience Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified remote sensing images lightweight detection head gain distribution strategy detection accuracy along activation function silu 3 %, respectively strengthens generalization capability significant resource consumption public datasets rsod head network incorporates approach achieved improvements high computational complexity 5 metric improved 3 %, 2 2 %, respectively algorithm &# 8217 6 %, 5 pconv </ p 6 %, smoke_fire_1 datasets significant enhancement neck network generalization experiments approach provides computational demand computational costs xlink "> using yolov8n scale attention receptive field parameter count ordinary quality nwpu vhr efficient multi easily lead detailed information complex backgrounds comparative experiments combines gsconv c2f within baseline algorithm anchor boxes
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GSConv working principle.

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