Pengenalan Golongan Jenis Kendaraan Bermotor pada Ruas Jalan Tol Menggunakan CNN

Ricky Herwanto, Kartika Gunadi, Endang Setyati

Abstract


Payment system at the toll gate has been improved, from using physical money replace with e-money. The system needs to which class types of the vehicle entering the toll gate so the system can know how much will it take from the e-money. There are five class types of vehicles, but there are still many toll gates that have high limit to limit the class of vehicles that can enter, making it difficult for class types other than the first class type because they only have a few gates. This research uses You Only Look Once and Convolutional Neural Network as its methods. You Only Look Once is used to detect the location of the vehicle in the image. Convolutional Neural Network is used to classify the class types of the vehicle in the image. For convolutional neural network model, one well-known model is VGG16 which is good in classifying images. The result of this research that will be displayed is the classified of the class type of the vehicle in the form of strings. The result from tests that were done is an accuracy of 93.5% and f-score of 81.37% from self-configuration convolutional neural network and an accuracy of 90.76% and f-score of 73.53% for VGG16 model.

Keywords


Convolutional Neural Network; VGG16; Class Types of Vehicles; You Only Look Once

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References


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Keputusan Menteri Pekerjaan Umum Nomor 370/KPTS/M/2007 tentang Penetapan Golongan Jenis Kendaraan Bermotor pada Ruas Jalan Tol yang sudah Beroperasi dan Besarnya Tarif Tol pada Beberapa Ruas Jalan Tol.

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