基于组合模型的车模识别算法研究  

Research on Car Model Recognition Algorithm Based on Combined Model

作　　者：张一博 茹禹然 赵文吕 王冬琦[1] ZHANG Yi-bo;RU Yu-ran;ZHAO Wen-lv;WANG Dong-qi(School of Software,Northeastern University,Shenyang 110000,China)

机构地区：[1]东北大学软件学院,辽宁沈阳110000

出　　处：《电脑知识与技术》2021年第28期15-19,共5页Computer Knowledge and Technology

基　　金：东北大学大学生创新训练计划自筹项目(201268);中央高校基本科研业务专项资金资助(N182410001)。

摘　　要：针对小比例车模识别中图像种类繁多、部分类间相似度较高、网络数据类别不均衡以及质量参差不一的问题,文章提出了一种组合模型。首先对网络采集的图像数据设计了一种基于深度学习的方法进行清洗,然后以破坏-重建学习(Destruction and Construction Learning)方法为基础结合文章提出改进的Class-Balanced Focal Loss权重调节方法构建细粒度识别模型,最后文章选取了3种评价指标对模型效果进行评价。实验结果表明,该组合模型相较于原方法能更加准确地对小比例车模进行识别,对于少数类具备更强的泛化能力。In order to solve the problems of many kinds of images,high similarity among some classes,unbalance of network data classes and variable quality in small scale vehicle model recognition,a combined model is proposed in this paper.Image data from the first to the network design a deep Learning based method for cleaning,and then to Destruction and Construction Learning meth⁃od combined with in this paper,on the basis of improving the Class-Balanced Focal Loss weight adjusting method to build fine grained recognition model,at the end of the paper analyzes three kinds of evaluation index to assess the economic impact on the model.Experimental results show that compared with the original method,the combined model can identify the small proportion car models more accurately,and has stronger generalization ability for a few classes.

关 键 词：深度学习 小比例车模 卷积神经网络 细粒度识别 不平衡数据集 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]