计算机工程与应用2024，Vol.60Issue(20)：233-243,11.DOI:10.3778/j.issn.1002-8331.2305-0513
结合YOLO-FGE网络的商标检测与分类
Trademark Detection and Classification Based on YOLO-FGE
摘要
Abstract
In order to solve the trademarks'problems about their numerous styles,complex backgrounds,and large-scale changes,a YOLO-FGE network model based on the YOLOv5 framework is proposed to distinguish trademark category information more accurately.Firstly,a feature enhancement module is put forward to enhance the adaptability of the fea-ture layer to different kinds of trademarks,making the network pay more attention to the useful information of trademarks to be detected.Secondly,the global information attention module is embedded in the C3 module of YOLOv5 to optimize the backbone and neck network.Finally,an enhanced spatial attention module is raised,which uses dilated convolution to expand the receptive field,combines channel attention and Transformer module to improve the detection accuracy.The experimental results on the graphic trademark dataset show that the model improves mAP to 92.3%,which has higher detection accuracy than most existing methods.关键词
商标检测/特征增强/全局注意力/空间注意力Key words
trademark detection/feature enhancement/global attention/spatial attention分类
信息技术与安全科学引用本文复制引用
缪春沅,王修晖..结合YOLO-FGE网络的商标检测与分类[J].计算机工程与应用,2024,60(20):233-243,11.基金项目
国家重点研发计划课题(2021YFC3340402). （2021YFC3340402）
