摘要
为了解决交通标志小目标检测所存在的漏检、误检和准确率低等问题,本文提出了一种小目标交通标志检测模型YOLOv8-Faster-Ghost-GAM。该算法首先在主干网络的最后一个C2f模块中引入了全局注意力机制(GAM),增强关键特征并抑制无关信息,显著提升了目标检测中的小目标和复杂场景下的识别能力;其次,将主干网络中的每个C2f模块替换为Fasternet,以减少模型参数量,并将普通卷积替换为幻影卷积Ghost,使用低廉的线性变换较少计算量;最后,采用WiOU损失函数,有效提升对低质量样本的识别,精度提升了1.6%,召回率提升了3.2%,证明了所作的改进的有效性。
In order to address the issues of missed detections,false positives,and low accuracy in small traffic sign detection,this paper proposes a detection model for small traffic signs,named YOLOv8-Faster-Ghost-GAM.The algorithm introduces a global attention mechanism(GAM)into the last C2f module of the backbone network,enhancing key features and suppressing irrelevant information to significantly improve the detection of small targets and the recognition capability in complex scenarios.Additionally,each C2f module in the backbone network is replaced with FasterNet to reduce the number of model parameters,and standard convolutions are replaced with Ghost convolutions,which use inexpensive linear transformations to reduce computational effort.Finally,the WiOU loss function is employed to effectively improve the recognition of low-quality samples,resulting in a 1.6%increase in precision and a 3.2%increase in recall,thereby demonstrating the effectiveness of the proposed improvements.
作者
韩东旭
谢雨飞
Han Dongxu;Xie Yufei(School of Intelligent Science and Technology,Beijing University of Civil Engineering and Architecture,Beijing 102616,China)
出处
《电子测量技术》
北大核心
2025年第6期28-37,共10页
Electronic Measurement Technology
基金
北京市教委科研项目(KM202110016007)资助。
