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复杂背景下车型识别分类器

Classifier for Recognition of Fine-Grained Vehicle Models under Complex Background

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摘要

细粒度车型图像的类间特征差异小,在复杂图片背景中识别干扰因素多。为提高模型在复杂背景中对图像的特征提取能力和识别准确度,提出了基于支持向量机(SVM)和深度卷积神经网络(DCNN)的分类器集成模型Softmax-SVM。它将交叉熵代价函数与hinge损失函数相结合,代替Softmax函数层,减少了过拟合的发生。同时,设计了一个10层的DCNN提取特征,避免了手工提取特征的难题。实验数据集为复杂背景下的27类精细车型图像,尤其还包含同一汽车厂商的相近车型。实验结果表明,在不进行大量预处理的前提下,Softmax-SVM分类器识别269张测试样本能够获得97.78%的准确率,识别每张样本的时间为0.759 s,明显优于传统模式识别方法和未改进前的DCNN模型。因此,基于DCNN的Softmax-SVM分类器能够适应环境的复杂变化,兼顾识别精度与效率,为复杂背景下的细粒度车型分类提供了实际参考价值。

Abstract

The feature difference among the images of fine-grained vehicle models is small and there exist many factors disturbing recognition under complex image background. To improve the feature extraction ability and the recognition accuracy of images under complex background, a classifier named Softmax-SVM is proposed based on deep convolutional neural network (DCNN) and support vector machine(SVM), in which the cross-entropy cost function is combined with the hinge loss function to replace the Softmax function layer, so that the over-fitting is avoided. Meanwhile, a 10-layer DCNN is designed to extract features automatically and the problem of manual extraction of features is also avoided. The experimental dataset consists of the images of 27 types of fine-gained vehicle models under complex background, especially of the similar models from the same car manufacturer. The experimental results show that the Softmax-SVM classifier can be used to recognize the 269 sample images without much emphasis on the pre-processing stages, and in the identification process, the accuracy rate is 97.78% and the time to identity each image is 0.759 s. The above model performs more efficiently than the traditional recognition methods and the unimproved DCNN models. In consequence, the Softmax-SVM classifier based on DCNN can adapt to the complex changes of environment and give consideration to both the recognition accuracy and efficiency, which provides practical reference value in the classification field of fine-gained vehicle models under complex background.

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补充资料

中图分类号:TP391

DOI:10.3788/LOP56.041501

所属栏目:机器视觉

基金项目:光电信息控制和安全技术重点实验室基金(614210701041705)

收稿日期:2018-08-16

修改稿日期:2018-08-21

网络出版日期:2018-09-04

作者单位    点击查看

张洁:河北工业大学电子信息工程学院, 天津 300401光电信息控制和安全技术重点实验室, 天津 300308
赵红东:河北工业大学电子信息工程学院, 天津 300401光电信息控制和安全技术重点实验室, 天津 300308
李宇海:光电信息控制和安全技术重点实验室, 天津 300308
闫苗:河北工业大学电子信息工程学院, 天津 300401
赵泽通:河北工业大学电子信息工程学院, 天津 300401

联系人作者:李宇海(695263295@qq.com)

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引用该论文

Zhang Jie,Zhao Hongdong,Li Yuhai,Yan Miao,Zhao Zetong. Classifier for Recognition of Fine-Grained Vehicle Models under Complex Background[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041501

张洁,赵红东,李宇海,闫苗,赵泽通. 复杂背景下车型识别分类器[J]. 激光与光电子学进展, 2019, 56(4): 041501

被引情况

【1】马俊成,赵红东,杨东旭,康晴. 飞机目标分类的深度卷积神经网络设计优化. 激光与光电子学进展, 2019, 56(23): 231006--1

【2】张苗辉,张博,高诚诚. 一种多任务的卷积神经网络目标分类算法. 激光与光电子学进展, 2019, 56(23): 231502--1

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