Reconocimiento automático de automóviles mediante machine learning

Resumen

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.