| With the rapid development of China’s economy and people growing demand for cars,car 4S shop is facing increasingly fierce competition.At the same time,computer technology is also developing rapidly.The automation and high-efficiency of the management based on the computer technology have become an important means for each car 4S shop to improve economic benefits.Therefore,this thesis studies the license plate recognition technology in complex scenes.Based on the implemented license plate recognition technology,car will be identified when it entered the area of car 4S shop,and the car 4S shop management system is designed and implemented through the design of QT.The main work of this thesis is as follows:(1)This thesis designed a license plate recognition method for complex scenes based on deep learning.Firstly,the YOLO model is trained as a license plate locator based on the built license plate data set.Secondly,the YOLO network structure is improved to improve the detection speed of the model.Then,the network structure of YOLO is improved to improve the detection speed of the model.Finally,we have chosen the method of character segmentation and recognition in HyperLPR to realize the recognition function of locating license plate,which achieved a good effect of license plate recognition complex scenes.(2)The management system of car 4S shop is designed and implemented.The system is mainly designed for the problems encountered in the management process of car 4S shop.The function modules of the system include:management module for customer information,license plate recognition module,reminder module for customer who make reservations in advance,auto parts management module,maintenance management module.Compared with the traditional car 4S shop management system,the system design added license plate recognition module and reservation module in complex scenes.It can effectively improve the management efficiency and customer service quality of 4S shop.. |
PDF Full Text Request