摘要
文章针对小目标检测存在的可利用特征少、定位精度要求高、数据集小目标占比少、样本不均衡和小目标对象聚集等问题,提出将coordinate attention注意力嵌入YOLOv5模型。Coordinate attention注意力机制通过获取位置感知和方向感知的信息,能使YOLOv5模型更准确地识别和定位感兴趣的目标。YOLOv5改进模型采用木虱和VisDrone2019数据集开展实验验证,实验结果表明嵌入coordinate attention能有效提高YOLOv5的算法性能。
Aiming at the problems of small target detection,such as few available features,requirement of high positioning accuracy,small proportion of small target in data set,unbalanced samples and small target aggregation,this paper proposes to embed coordinate attention into YOLOv5 model.Coordinated attention mechanism can enable YOLOv5 model to identify and locate interested targets more accurately by obtaining information of location awareness and direction awareness.The improved YOLOv5 model uses psyllid and VisDrone 2019 datasets to carry out experiments to verify,and the experimental results show that embedding coordinate attention can effectively improve the algorithm performance of YOLOv5.
作者
陈富荣
肖明明
CHEN Furong;XIAO Mingming(College of Information Science and Technology,Zhongkai University of Agricultural and Engineering,Guangzhou 510225,China;College of Information and Communication Engineering,Guangzhou Maritime University,Guangzhou 510725,China)
出处
《现代信息科技》
2023年第3期55-60,65,共7页
Modern Information Technology
