A profusion of such applications has been developed to detect, identify, and track various objects of interest. 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.Ĭomputer vision applications automate repetitive tasks that require the human ability and attention to continuously monitor and make timely decisions. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. 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. 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. 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. It is critical to collect relevant data from the location where the application will be deployed. These use cases can be found in a variety of contexts and locations. In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets.
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