Lightweight improved RT-DETR algorithm for grape leaf disease detection

doi:10.3785/j.issn.1008-973X.2026.03.016

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (3): 604-613 DOI: 10.3785/j.issn.1008-973X.2026.03.016

Lightweight improved RT-DETR algorithm for grape leaf disease detection

Hui LIU(

),Fangxiu WANG*(

),Yi WANG,Zibo HUANG,Chen SU

School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China

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Abstract

A lightweight detector SCGI-DETR was proposed based on an enhanced RT-DETR in order to address challenges in grape leaf disease detection—complex background, missed detection of small target, and resource-constrained deployment. The efficient StarNet backbone was employed to reduce parameter count and computational cost, enabling lightweight deployment. A feature pyramid CGSFR-FPN was designed. Spatial feature reconstruction was combined with multi-scale feature fusion in order to strengthen global context modeling and improve localization of multi-scale lesions in cluttered scenes. The Inner-PowerIoU v2 loss was constructed, which integrated global convergence acceleration and local region alignment in order to speed up bounding-box regression and enhance small-object detection performance. SCGI-DETR attained 91.6% precision, 89.8% recall and 93.4% mAP@0.5 on a grape leaf disease dataset, which improved 2.6, 2.4 and 2.3 percentage points over the baseline, and reduced parameters and computation by 46.2% and 64%, respectively. Results demonstrate that the improved algorithm achieves lightweight implementation while delivering superior detection performance, meeting deployment requirements for mobile and embedded devices.

Key words： grape leaf disease RT-DETR StarNet feature pyramid lightweight network

Received: 25 May 2025 Published: 04 February 2026

CLC:

TP 391

Fund: 湖北省高校优秀中青年科技创新团队资助项目(T2021009)；湖北省教育厅科技计划资助项目(D20211604).

Corresponding Authors: Fangxiu WANG E-mail: 2283298757@qq.com;wfx323@whpu.edu.cn

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Cite this article:

Hui LIU,Fangxiu WANG,Yi WANG,Zibo HUANG,Chen SU. Lightweight improved RT-DETR algorithm for grape leaf disease detection. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 604-613.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.03.016 OR https://www.zjujournals.com/eng/Y2026/V60/I3/604

轻量级改进RT-DETR的葡萄叶片病害检测算法

针对葡萄叶片病害检测任务中存在的复杂背景干扰、小目标漏检及模型部署资源受限等问题，提出基于改进RT-DETR的轻量化检测算法SCGI-DETR. 引入高效轻量级的StarNet架构作为特征提取网络，减少模型的参数量和计算量，实现模型的轻量化. 设计CGSFR-FPN特征金字塔网络，通过空间特征重建和多尺度特征融合策略，增强模型对全局上下文信息的感知能力，提升复杂背景下多尺度病斑的定位精度. 构建Inner-PowerIoU v2损失函数，利用全局收敛加速与局部区域对齐机制，加速边界框回归，提高小目标检测性能. 实验结果表明，SCGI-DETR在葡萄叶片病害数据集上的精确率、召回率和mAP@0.5分别为91.6%、89.8%和93.4%，较原模型分别提升了2.6%、2.4%和2.3%，参数量与计算量分别减少了46.2%和64%. 该结果表明，改进算法在实现轻量化的同时，具备更优的检测性能，满足移动端和嵌入式设备的部署需求.

关键词： 葡萄叶片病害, RT-DETR, StarNet, 特征金字塔, 轻量化网络

Fig.1 Sample picture of grape leaf disease

Tab.1 Number of images in grape disease dataset

Fig.2 Structure diagram of SCGI-DETR network

Fig.3 Structure diagram of StarNet network

Fig.4 Structure of RCM module

Fig.5 Structure of PCE module

Fig.6 Structure diagram of Inner-PowerIoU v2

Tab.2 Comparison result of ablation experiment

Tab.3 Comparison result of different lightweight backbone network

Tab.4 Effect of different loss function on SCGI-DETR

Tab.5 Effect of different scale factor on SCGI-DETR

Tab.6 Comparison experimental result of different models

Fig.7 Comparison of small target detection effect

Fig.8 Comparison of detection effect under complex background

Fig.9 Visualization result of SCGI-DETR model’s ability to focus on key area of blade


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