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基于卷积神经网络的多任务学习用于高度相似车型分类

CNN-Based Classification for Highly Similar Vehicle Model Using Multi-Task Learning.

作者信息

Avianto Donny, Harjoko Agus

机构信息

Department of Informatics, Universitas Teknologi Yogyakarta, Yogyakarta 55285, Indonesia.

Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.

出版信息

J Imaging. 2022 Oct 22;8(11):293. doi: 10.3390/jimaging8110293.

Abstract

Vehicle make and model classification is crucial to the operation of an intelligent transportation system (ITS). Fine-grained vehicle information such as make and model can help officers uncover cases of traffic violations when license plate information cannot be obtained. Various techniques have been developed to perform vehicle make and model classification. However, it is very hard to identify the make and model of vehicles with highly similar visual appearances. The classifier contains a lot of potential for mistakes because the vehicles look very similar but have different models and manufacturers. To solve this problem, a fine-grained classifier based on convolutional neural networks with a multi-task learning approach is proposed in this paper. The proposed method takes a vehicle image as input and extracts features using the VGG-16 architecture. The extracted features will then be sent to two different branches, with one branch being used to classify the vehicle model and the other to classify the vehicle make. The performance of the proposed method was evaluated using the InaV-Dash dataset, which contains an Indonesian vehicle model with a highly similar visual appearance. The experimental results show that the proposed method achieves 98.73% accuracy for vehicle make and 97.69% accuracy for vehicle model. Our study also demonstrates that the proposed method is able to improve the performance of the baseline method on highly similar vehicle classification problems.

摘要

车辆品牌和型号分类对于智能交通系统(ITS)的运行至关重要。当无法获取车牌信息时,诸如品牌和型号等细粒度车辆信息可以帮助执法人员发现交通违规案件。人们已经开发了各种技术来进行车辆品牌和型号分类。然而,识别视觉外观高度相似的车辆的品牌和型号非常困难。由于车辆外观非常相似但型号和制造商不同,分类器很容易出错。为了解决这个问题,本文提出了一种基于卷积神经网络和多任务学习方法的细粒度分类器。该方法以车辆图像为输入,使用VGG-16架构提取特征。然后将提取的特征发送到两个不同的分支,一个分支用于对车辆型号进行分类,另一个分支用于对车辆品牌进行分类。使用InaV-Dash数据集对该方法的性能进行了评估,该数据集包含视觉外观高度相似的印度尼西亚车辆模型。实验结果表明,该方法在车辆品牌分类上的准确率达到98.73%,在车辆型号分类上的准确率达到97.69%。我们的研究还表明,该方法能够在高度相似的车辆分类问题上提高基线方法的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8e9/9697843/57e694ebb753/jimaging-08-00293-g001.jpg

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