Analysis and Architecture for the deployment of Dynamic License Plate Recognition Using YOLO Darknet

Abstract

Dynamic License Plate Recognition (DLPR) has been a successive subject of research because of numerous functional applications. Be that as it may, a significant number of the present arrangements are still not reliable in certifiable circumstances, usually relying upon innumerable limitations. This paper exhibits an active and productive DLPR framework and architecture, which can be implemented for dynamic license plate identification based on the best in class YOLO object identifier. The Convolutional Neural Networks (CNN) are prepared and calibrated so that their robustness is sustained under diverse setups (e.g., varieties in the camera, illumination, and foundation). Extraordinarily for character segregation and identification, we structure a methodology utilizing straightforward information enlargement instances, for example, reversed LPs and inverted characters. The subsequent DLPR modus operandi accomplished noteworthy outcomes in the data sets. Our test results show that the proposed strategy and deployment architecture, with no parameter adjustment, performs exceptionally well on the data collected dynamically from a video using a raspberry pi and has been successful in identifying multiple license plates and extracting the characters, the process, however, is time exhaustive.