Abstract
In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things.
Highlights
Computer vision applications automate repetitive tasks that require the human ability and attention to continuously monitor and make timely decisions
In an ever-increasingly complex urban environment, we propose an all-in-one system named KVT-LPR which stands for Korean vehicle type and license plate recognition system, capable of identifying both vehicle types (VT) and license plates (LP) in the same processing pipeline
In contrast to prior research, this study investigates the application of YOLOv4 for LPR and vehicle type recognition in the Korean environment with multilanes and highresolution cameras
Summary
Computer vision applications automate repetitive tasks that require the human ability and attention to continuously monitor and make timely decisions. A series of state-of-the-art deep learning techniques for challenging computer vision problems [5] can detect and identify a vast number of diverse objects across categories on a grand scale. Individuals and their vehicles are significant subjects of interest in large cities and metropolitan regions, which smart cameras try to recognize.
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