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%.
Highlights
Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life
Machine Learning (ML) plays important role in Computer Vision (CV) due to its capability to learn the pattern of objects or images and classify the object that is taken by camera
We developed a simple vehicle counting system using Deep Learning algorithm
Summary
Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. A CV system needs preprocessing and feature extraction step before it can detect, classify, or recognize objects within the image using ML algorithm [1]–[3]. While Deep Learning (DL) approach gives more flexible performance without having to preprocess the image and extract the feature using several methods, even though it is computationally expensive, and it needs large amount of data to train the networks. In this work we focus on speed and 4 GB of VRAM This engine is not developing a system that count the number of vehicles as fast as the GTX version from Nvidia, but it is quite crossing the road where the counting is based on the type enough to run the Deep Learning-based system. From of the vehicle itself, i.e. car, bus, truck, and motorcycle our observation, it can detect the objects for about 0.2 –
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