南京师范大学学报（工程技术版）2025，Vol.25Issue(2)：69-78,10.DOI:10.3969/j.issn.1672-1292.2025.02.006
基于改进RT-DETR的轻量化交通标志检测方法
Lightweight Traffic Sign Detection Based on Improved RT-DETR
摘要
Abstract
Aiming at the problem of small traffic sign targets and large model volume,a lightweight traffic sign detection based on improved RT-DETR(real-time detection transformer)is proposed.Firstly,the PConv(partial convolution)convolution optimization BasicBlock structure is introduced into the backbone network,and the PC_Block module is constructed,which enhances the feature extraction capability and reduces the number of parameters and computation.Secondly,the DyASF(dynamic attentional scale sequence fusion)module,which fuses multi-scale features in an adaptive way to improve the model's spatial perception and feature expression ability.Then,using the Focaler-MPDIoU loss function,the similarity between the predicted bounding box and the real bounding box is accurately evaluated by focusing on different regression samples and introducing the distance of the minima,which improves the detection performance of the model.Finally,the P2 detection layer is introduced that improves the detection of small targets and enhances the model robustness.Comparison experiments are conducted on the public dataset TT100K,and the results show that the improved model reduces the number of parameters by 53.8%,the computation amount by 22.8%,and the detection accuracy by 2.4%,which can better meet the requirements of traffic sign detection.关键词
目标检测/轻量化/交通标志检测/多尺度特征/RT-DETRKey words
target detection/lightweight/traffic sign detection/multi-scale features/RT-DETR分类
计算机与自动化引用本文复制引用
耿嘉雯,严云洋,朱妍,于永涛,叶翔,董可..基于改进RT-DETR的轻量化交通标志检测方法[J].南京师范大学学报（工程技术版）,2025,25(2):69-78,10.基金项目
国家自然科学基金项目(62076107)、江苏省"六大人才高峰"项目(2013DZXX-023). （62076107）
