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Convergence CCTV camera embedded with Deep Learning SW technology

딥러닝 SW 기술을 이용한 임베디드형 융합 CCTV 카메라

Son, Kyong-Sik;Kim, Jong-Won;Lim, Jae-Hyun
손경식;김종원;임재현

  • Received : 2019.10.26
  • Accepted : 2019.12.20
  • Published : 2019.01.28

Abstract

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

Keywords

LPR Camera;CCTV;convergence;Edge base;Embedded;Image Analysis Module;Deep Learning SW Technology