A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Abstract

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%.

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

“…They are also readily available, computationally inexpensive and show good performance metrics. Object recognition systems from the YOLO family [51,52] are often used for vehicle recognition tasks, e.g., [27][28][29]37] and have been shown to outperform other target recognition algorithms [53,54]. YOLOv5 has proven to significantly improve the processing time of deeper networks [50].…”

“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Efficient Video-based Vehicle Queue Length Estimation using Computer Vision and Deep Learning for an Urban Traffic Scenario

“…YOLO has so far received five upgradations, with YOLOv5 being the latest one and having best performance. YOLO is often used for various vehicle related recognition tasks and has shown significant improvements in terms of processing time and accuracies [31][32][33][34].…”

Detecção De Animais Bovinos Utilizando Imagens Aéreas Por Meio De Redes Neurais

“…A YOLOv3 contém ao todo 53 camadas convolucionais como apresentado na Figura 5. Com esta arquitetura, houve melhorias tanto na acurácia da detecção de objetos quanto na otimização do uso da GPU, tornando-se mais eficiente o uso computacional (FACHRIE, 2020).…”