山东农业科学2026，Vol.58Issue(3)：160-170,11.DOI:10.14083/j.issn.1001-4942.2026.03.019
基于RT-DETR改进的幼桃识别模型
Improved Immature Peach Recognition Model Based on RT-DETR
摘要
Abstract
In response to the challenges of identifying immature peaches in natural environments,such as color similarity with surrounding environments,uneven lighting,and obstruction by branches and leaves,a detection model FREDC-RTDETR was proposed based on improving RT-DETR-R18 in this study.By replacing the BasicBlock in the RT-DETR-R18 backbone network with the Faster NetBlock,incorporating RepConv reparameterization technology,and introducing the EMA attention mechanism,a new backbone network FRE Block was designed,which could reduce the number of parameters but enhancing the model's feature extrac-tion capability.In the neck network,the original AIFI module was replaced with AIFI-LPE based on learnable position encoding to address the issue of attention shift,and the DySample dynamic upsampling along with the redesigned CG block Down downsampling operator were employed to optimize the upsampling and downsam-pling processes.Additionally,the Shape-IoU loss function was used to enhance the model's ability to capture image details.The experimental results showed that on the self-built dataset,the improved model achieved the mean average precision of 96.1％,the recall rate of 91.9％,and the precision of 97.6％,representing increa-ses of 2.4,2.7 and 2.5 percentage points compared to the original model,respectively.In conclusion,the pro-posed model in this study demonstrated better robustness and accuracy in complex backgrounds,which could provide a reference for early yield prediction and green fruit identification of fruit trees.关键词
幼桃识别/RT-DETR/FRE Block/AIFI-LPE模块/DySample动态上采样/CG block Down下采样/Shape-IoU损失函数Key words
Immature peach recognition/RT-DETR/FRE Block/AIFI-LPE module/DySample dy-namic upsampling/CG block Down sampling/Shape-IoU loss function分类
农业科技引用本文复制引用
张云建,陈红明,杨晓刚,杨灿鹏,王学睿,黄中豪,杨琳琳..基于RT-DETR改进的幼桃识别模型[J].山东农业科学,2026,58(3):160-170,11.基金项目
国家自然科学基金项目(32160420) （32160420）
云南省重大科技专项(202202AE09002103) （202202AE09002103）
云南省农林联合专项(202301BD070001-172) （202301BD070001-172）
