Lightweight Improvements Based on RT-DETR

Tianjun Yi

doi:10.47297/taposatWSP2633-456921.20260706

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Lightweight Improvements Based on RT-DETR

Volume 7, Issue 6

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Lightweight Improvements Based on RT-DETR
Lightweight Improvements Based on RT-DETR
School of Physics and Electronic Sciences, Changsha University of Science and Technology, Changsha, Hunan, 410000, China
Vol. 7, Issue 6, Pages: 102-108(2026)
Online First：18 August 2026，

Published：30 June 2026
DOI：10.47297/taposatWSP2633-456921.20260706
Accepted：

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Yi Tianjun.Lightweight Improvements Based on RT-DETR[J].Theory and Practice of Science and Technology,2026,07(06):102-108.
DOI：

Yi Tianjun.Lightweight Improvements Based on RT-DETR[J].Theory and Practice of Science and Technology,2026,07(06):102-108. DOI： 10.47297/taposatWSP2633-456921.20260706.

摘要

Abstract

This work suggests a lightweight approach that combines structural optimization with model compression to address the problems of the RT-DETR model

specifically its high computational cost

huge number of parameters

and difficulties in deploying it on edge devices. By incorporating the Ghost backbone network and CGC3 modules

we enhance inference efficiency and feature representation capabilities

and further reduce the model size through pruning

quantization

and model distillation. According to the experimental results

the modified model greatly reduces the number of model parameters while achieving a minor increase in mAP50 and AP50-95 on the VOC2012 dataset. Furthermore

when deployed on an edge computing platform

the model demonstrates a significant improvement in FPS

validating its effectiveness.

关键词

Keywords

references

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