@article{2972,
  author = {Ankith C Kowshik, Anil Kumar B, B P Gagan Deepa, Anoop S, Asha T},
  title = {YOLO based License Plate Detection using CNN},
  journal = {Transactions on Machine Design},
  year = {2020},
  volume = {8},
  number = {1},
  doi = {},
  url = {http://www.dline.info/tmd/fulltext/v8n1/tmdv8n1_1.pdf},
  abstract = {This research paper is based on you only look once (YOLO) object detection algorithm that detects and
recognizes Indian license plates on conventional environments. Traffic control and management has been rising problems in urban conditions and several attempts have been already made to mitigate it using different methods. The safety as well as smooth flow of traffic is very important. Currently such a problem lacks an elegant solution. By using state-of-the-art object detection technique combined with the configured convolutional neural network (CNN) built using Tensor flow core open
source libraries, real-time detection and recognition has been achieved. Although license plate detection as well as recognition had been widely adopted by enormous number of countries around the world for surveillance purposes it remains as a significant challenge in India, where the size of the number plates on Indian vehicles are not fixed and the CCTV used are not of high resolution. This paper tries to solve this problem by using a version of YOLOv2 (improved version of YOLO). The
results of this achieved acceptable real time detection up to mean average of 9.8 FPS when running in Nvidia GT 940M graphics card.},
}