A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model
Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Tr...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2020-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1871 |
| _version_ | 1797333871083126784 |
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| author | Muhammad Fachrie |
| author_facet | Muhammad Fachrie |
| author_sort | Muhammad Fachrie |
| collection | DOAJ |
| description | Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%. |
| first_indexed | 2024-03-08T08:12:18Z |
| format | Article |
| id | doaj.art-4165b448f3804569b5aeb116ae58c1a0 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T08:12:18Z |
| publishDate | 2020-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-4165b448f3804569b5aeb116ae58c1a02024-02-02T08:20:09ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602020-06-014346246810.29207/resti.v4i3.18711871A Simple Vehicle Counting System Using Deep Learning with YOLOv3 ModelMuhammad Fachrie0Universitas Teknologi YogyakartaDeep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.http://jurnal.iaii.or.id/index.php/RESTI/article/view/1871deep learningyolov3object detectionvehicle countingtraffic monitoring |
| spellingShingle | Muhammad Fachrie A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) deep learning yolov3 object detection vehicle counting traffic monitoring |
| title | A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model |
| title_full | A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model |
| title_fullStr | A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model |
| title_full_unstemmed | A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model |
| title_short | A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model |
| title_sort | simple vehicle counting system using deep learning with yolov3 model |
| topic | deep learning yolov3 object detection vehicle counting traffic monitoring |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1871 |
| work_keys_str_mv | AT muhammadfachrie asimplevehiclecountingsystemusingdeeplearningwithyolov3model AT muhammadfachrie simplevehiclecountingsystemusingdeeplearningwithyolov3model |