基于混合注意力机制的细粒度车辆分类识别研究  

Research on Fine-grained Vehicle Classification Recognition Based on Mixed Attention Mechanism

作　　者：尚亚博 贾得顺 SHANG Yabo;JIA Deshun(School of Automotive and Rail Transit,Luoyang Vocational and Technical College,Luoyang 471023,China)

机构地区：[1]洛阳职业技术学院汽车与轨道交通学院,河南洛阳471023

出　　处：《汽车实用技术》2025年第7期35-40,共6页Automobile Applied Technology

摘　　要：针对细粒度图像分类任务中存在的两大挑战:一是区分性特征极其细微,难以准确捕捉;二是难以有效定位图像中感兴趣的关键区域。文章提出一种基于混合注意力机制的细粒度车辆分类识别模型,通过充分整合局部细节信息与全局上下文信息,显著提升了对细粒度车辆类别的辨识能力。通过实验结果表明,提出的注意力机制模型在斯坦福汽车(Stanford Cars)和网络汽车(Web-Cars)公开车辆数据集上分类准确度分别达到94.2%和88.0%,相较于其他网络模型,该模型展现出更高的分类准确率和更强的泛化能力,能够有效弥补传统卷积神经网络存在的不足。There are two challenges in the task of fine-grained image classification:One is that the distinguishing features are extremely subtle and difficult to capture accurately.Second,it is difficult to effectively locate the key areas of interest in the image.In this paper,a fine-grained vehicle classification recognition model based on mixed attention mechanism is proposed.By fully integrating local details and global context information,the recognition ability of fine-grained vehicle categories is significantly improved.The experimental results show that the classification accuracy of the proposed attention mechanism model on the Stanford Cars and Web-Cars open vehicle data sets reaches 94.2%and 88.0%,respectively.Compared with other network models,this model shows higher classification accuracy and stronger generalization ability.It can effectively make up for the shortcomings of traditional convolutional neural networks.

关 键 词：细粒度分类 深度学习 注意力机制 特征融合 

分 类 号：U467[机械工程—车辆工程]