Abstract

Detection and classification of vehicles are inseparable parts of Intelligent Transportation Systems (ITS), various kinds of information technology applications are used to be able to detect and classify these vehicles, starting with the use of ultrasonic sensors, laser scanners, induction loops, magnetic sensors, range sensors, pressure sensors and CCTV cameras, but the circulation of vehicles with the same design from different manufacturing companies makes the classification of vehicles to determine the vehicle brands and types difficult to do. In this paper, deep learning framework Mask Regional-Convolutional Neural Network (Mask R-CNN) is used to solve the problem. Experiments have been conducted twice by using a combination of different datasets and detection algorithms. To be able to distinguish cars with similar shapes from different manufacturers, we use vehicle logos as one of the features that distinguish the manufacturer. The best detection and classification results were obtained in dataset training using 60 epoch, 400 step iterations with an accuracy value of 0.91 and mAP (Mean Average Precision) of 0.89.

Original languageEnglish
Title of host publicationProceedings - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages422-427
Number of pages6
ISBN (Electronic)9781728137490
DOIs
Publication statusPublished - Aug 2019
Event2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019 - Surabaya, Indonesia
Duration: 28 Aug 201929 Aug 2019

Publication series

NameProceedings - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019

Conference

Conference2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019
Country/TerritoryIndonesia
CitySurabaya
Period28/08/1929/08/19

Keywords

  • Car
  • Deep Learning
  • Detection
  • Logo
  • Mask R-CNN
  • Vehicle

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