使用机器学习的汽车自动识别

Q3 Engineering Dyna-Colombia Pub Date : 2023-07-07 DOI:10.6036/10673
R. Guzmán Cabrera, Deborah Martínez, M. TORRES CISNEROS, D. MAY ARRIOJA, Mary Carmen PEÑA GOMAR
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引用次数: 0

摘要

在这项工作中,我们对五种不同车型的1000幅图像进行了自动分类。为了获得最高精度,我们使用了两种不同的分类场景、三种算法和五种度量。此外,我们假设可以通过使用描述符提取图像特征并将其用作输入来改进结果。然后,我们使用了两个描述符:定向梯度的直方图和卷积神经网络ResNet-50。我们的结果表明,使用ResNet-50作为描述符,使用训练和测试集作为场景,使用向量支持机作为分类算法,描述符改进了分类结果,并获得了88.01%的准确度度量的最高值。关键词:卷积神经网络,梯度直方图,机器学习,细粒度分类,汽车图像。
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AUTOMATIC RECOGNITION OF AUTOMOBILES USING MACHINE LEARNING
In this work, we perform the automatic classification of 1,000 images of five different models of automobiles. To obtain the highest precision, we have used two different classification scenarios, three algorithms, and five metrics. Also, we assume that the results can be improved by extracting the image characteristics using descriptors and using them as input. Then, we used two descriptors: a histogram of oriented gradient and a convolutional neural network ResNet-50. Our results show that the descriptors improve the classification results and obtain the highest value for the accuracy metric of 88.01 % using the ResNet-50 as a descriptor, the Training and Test Set as a scenario, and Vector Support Machine as the classification algorithm. Keywords: Convolutional Neural Networks, Gradient Oriented Histogram, Machine Learning, Fine Grain Classification, Car Images.
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来源期刊
Dyna-Colombia
Dyna-Colombia 工程技术-工程:综合
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
4-8 weeks
期刊介绍: The DYNA journal, consistent with the aim of disseminating research in engineering, covers all disciplines within the large area of Engineering and Technology (OECD), through research articles, case studies and review articles resulting from work of national and international researchers.
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