华南理工大学学报（自然科学版）2026，Vol.54Issue(3)：65-78,14.DOI:10.12141/j.issn.1000-565X.250092
基于CGT-YOLO的小目标交通标志识别算法
CGT-YOLO-Based Algorithm for Small-Target Traffic Sign Recognition
摘要
Abstract
To address the degradation in recognition accuracy caused by false and missed detections of small target traffic signs,this study proposes a small traffic sign recognition algorithm based on CGT-YOLO.First,a context-aware enhancement module(CAM)is introduced to replace the spatial pyramid pooling fast(SPPF)module in the YOLOv5s network.By employing parallel dilated convolutions with different dilation rates,the CAM enhances mul-tiscale feature representation and contextual information of small traffic signs without reducing spatial resolution.Second,a global attention mechanism(GAM)is inserted after the concatenation operation in the backbone network of YOLOv5s.The GAM extracts features enhanced by the CAM and strengthens global interaction between channel and spatial dimensions through 3D permutation,multi-layer perceptron,and convolutional spatial attention,thereby highlighting the features of small traffic signs and mitigating the negative effects of complex backgrounds and long distances.Finally,a task-specific context(TSC)decoupled head is utilized to separate features for classification and localization tasks.Through the semantic context encoder(SCE)and detail preservation encoder(DPE)modules,the head generates semantically rich low-resolution feature maps for classification and high-resolution feature maps containing boundary information for localization,respectively.This disentangles classification and localization tasks at the feature source,resolving feature conflicts between the two tasks for small target traffic signs.Experi-mental results on a dataset constructed by integrating TT100K and CCTSDB show that the improved model achieves enhanced performance across all metrics:the missed detection rate and false detection rate are reduced by 12.1 and 11.6 percentage points,respectively,while mAP(0.50:0.95)increases by 0.026 0.Compared to models such as YOLOv8s,NanoDet-Plus,and RT-DETR-Nano,CGT-YOLO demonstrates superior performance across multiple metrics.While maintaining a high inference speed(72.5 FPS),it effectively reduces false and missed detections,significantly improving the detection accuracy and robustness of small target traffic signs in complex scenarios.关键词
小目标识别/交通标志识别/膨胀卷积/注意力机制/解耦头Key words
small target recognition/traffic sign recognition/dilated convolution/attention mechanism/decoupled head分类
信息技术与安全科学引用本文复制引用
邢岩,郭思豪,张振,潘晓东,安冬..基于CGT-YOLO的小目标交通标志识别算法[J].华南理工大学学报（自然科学版）,2026,54(3):65-78,14.基金项目
道路交通安全管控技术国家工程研究中心开放课题(2024GCZXKFKT13B)Supported by the Open Project of National Engineering Research Center for Road Traffic Safety Control Technology(2024GCZXKFKT13B) （2024GCZXKFKT13B）
