Automatic recognition of automobiles using machine learning

被引：0

作者：

Martinez-Camacho, Deborah G. ^{[1
]}

Torres-Cisneros, Miguel ^{[2
]}

May-Arrioja, Daniel A. ^{[1
]}

Pena-Gomar, Mary-Carmen ^{[3
]}

Guzman-Cabrera, Rafael ^{[2
]}

机构：

[1] Ctr Invest Opt AC, Calle Prol Constituc 607,Fraccionamiento Reserva, Aguascalientes 20200, Aguascalientes, Mexico

[2] Univ Guanajuato, Fis Aplicada & Tecnol Avanzadas, Campus Irapuato Salamanca, Salamanca 36885, Mexico

[3] Univ Michoacana, FCFM, C Santiago Tapia 403,Ctr, Morelia 58000, Michoacan, Mexico

来源：

DYNA | 2023年 / 98卷 / 05期

关键词：

Convolutional Neural Networks; Gradient Oriented Histogram; Machine Learning; Fine Grain Classification; Car Images;

D O I：

10.6036/10673

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：511 / 516

页数：6

共 18 条

[1]

Blanc N, 2007, 2007 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1-3, P1097

[2] Accurate seat belt detection in road surveillance images based on CNN and SVM [J].

Chen, Yanxiang ;

Tao, Gang ;

Ren, Hongmei ;

Lin, Xinyu ;

Zhang, Luming .

NEUROCOMPUTING, 2018, 274 :80-87

[3] Histograms of oriented gradients for human detection [J].

Dalal, N ;

Triggs, B .

2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, :886-893

[4] Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV [J].

de Oliveira, Diulhio Candido ;

Wehrmeister, Marco Aurelio .

SENSORS, 2018, 18 (07)

[5] Adversarial erasing attention for fine-grained image classification [J].

Ji, Jinsheng ;

Jiang, Linfeng ;

Zhang, Tao ;

Zhong, Weilin ;

Xiong, Huilin .

MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (15) :22867-22889

[6] Combining shape-based and gradient-based classifiers for vehicle classification [J].

Karaimer, Hakki Can ;

Cinaroglu, Ibrahim ;

Bastanlar, Yalin .

2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, :800-805

[7] Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina [J].

Lahmiri, Salim .

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 60

[8] Bilinear CNN Models for Fine-grained Visual Recognition [J].

Lin, Tsung-Yu ;

RoyChowdhury, Aruni ;

Maji, Subhransu .

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :1449-1457

[9] A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine [J].

Ozyurt, Fatih .

SOFT COMPUTING, 2020, 24 (11) :8163-8172

[10] Deep Learning Based Car Damage Classification [J].

Patil, Kalpesh ;

Kulkarni, Mandar ;

Sriraman, Anand ;

Karande, Shirish .

2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, :50-54

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