Title: Car Make and Model Classification with Deep Learning Methods
Authors: Lucas Albini, Matheus Gutoski, Heitor Silvério Lopes
Abstract: Car make and model classification is an issue frequently discussed in the literature due to its several applications in security, traffic control, and urban planning, especially in the context of smart cities. Currently, deep learning methods are the state-of-the-art for image and video classification. This work it is presented a method for classifying cars at the level of make and model in a simple and effective way using deep learning methods. To accomplish this task, the Inception-v3 neural network was used to train and evaluate the model. Another objective of this work is to create a high-quality dataset of images of cars produced by the Brazilian industry. The full dataset has 24319 images distributed into 10 makes and 50 models, with an average of 500 images per class. The average classification accuracy reached 82.36% and 94,87%, when considering the top-3 results. Our results showed that the proposed approach was very successful for classification purposes and encourages further development.
Key-words: Image Segmentation; Evolutionary Computation; Genetic Expression Programming; Color recognition;
DOI code: 10.21528/CBIC2019-103
PDF file: CBIC2019-103.pdf
BibTeX file: CBIC2019-103.bib