VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY ADVANCED PROGRAM IN INFORMATION SYSTEMS TRAN DUC THINH BUI TAN LOC THESIS GRADUATION A MOBILE-BASED APPLICATION FOR SMART TINY PARKING LOT BACHELOR OF ENGINEERING IN INFORMATION SYSTEMS HO CHI MINH CITY, 2023 VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY ADVANCED PROGRAM IN INFORMATION SYSTEMS TRAN DUC THINH - 18521450 BUI TAN LOC- 18521002 THESIS GRADUATION A MOBILE-BASED APPLICATION FOR SMART TINY PARKING LOT BACHELOR OF ENGINEERING IN INFORMATION SYSTEMS THESIS ADVISOR Ph. NGUYEN THANH BINH HO CHI MINH, 2023 ASSESSMENT COMMITTEE The assessment committee is established under the Decision. by Rector of the University of Information Technology. eee eee eee.
eee nee eee en ee — Member. ACKNOWLEDGMENTS For this graduate thesis, we would like to express our sincere gratitude to my advisor, Ph. Nguyen Thanh Binh, who wholeheartedly taught and supported us during our university studies and completion of this graduate thesis. In addition to teaching, suggesting knowledge for the thesis, giving presentations, and commenting on everything, he also cares about students' psychology, and listens, inspires, and motivates us.
Without her support and advice, we probably wouldn't have done my best job. At the same time, we also want to send to the teachers and teaching assistants at the University of Information Technology, especially the teachers in the Faculty of Information Systems, who have imparted valuable knowledge to me during these 4 years here. Therefore, we have improved ourselves and are ready for the future career path. We consider this an important milestone in my education and development.
We will try to use the skills and knowledge gained in the best possible way and we will continue to improve more in the future. Once again, we would like to thank everyone for giving me the motivation and creating the most convenient conditions for us to complete the graduation thesis. Sincerely, Tran Duc Thinh Bui Tan Loc TABLE OF CONTENTS Chapter 1.cccccecescesessesesesesteseseseeseseesesesneenenes 3 lì No ha. Aims and ObJ€CfIV€S:.
¿+ 1t 1121211 T HH HH HH HH 4 1. Scope Of StUdy. cành HH HH HH HH TH Hư4 1. Structure of th€SiS.
THEORETICAL BACKGROUND AND LITERATURE REVIEW. Solutions available on the market. Framework and technOÏOgIGS. Near field communication (NFC).
Automatic license plate đeteCtiOn. Automatic license plate reCOgnitiOn. METHOD AND EXPERIMENT RESULTS OF LICENSE PLATE RECOGNITION. Automatic license plate detection model.
Preparing experiment đa(aSe(. _ Image annotat(iOn. Single shot detector MobileNet V2 (SSD MobileNet V2). Automatic license plate detection model.
Evaluate detection model oo. Auto License Plate Recognition method. Convert TensorFlow model to TensorFlow LIte. __ CameraX and image analySis.
Object cropping and grayscale. Text recognition — Machine learning KÍt. DESIGN AND DEVELOPMENT MOBILE APPLICATION. HH HH HH HH it 36 4.
System analyze design. Use case diagram. Use case specification. SE 1121 1E HH gi60 4.
User interface deSign. cành ng Hy 63 Chapter 5. 2 GB BESEE Server81 LIST OF FIGURES Figure 2-1 Smart parking system Simulation css| Figure 2-2 Compare NFC and RFID frequency. LL Figure 2-3 Principle NFC architecture Figure 2-4 Working principle of the automatic road toll collection system.13 Figure 2-5 Types of NFC tag on Idrkef.ccă «cu LA Figure 3-1 Full process of License Plate Detection and Recognifion.
18 Figure 3-2 Stage 1: License Plate Detection pr0C€§S. LB Figure 3-3 Dataset sdIHpÏe. sieu LO Figure 3-4 Labellmg annotation tool to label map image. 21 Figure 3-5 SSD MobileNet v2 Archiite€fIIFr€.--e-sceeeceeeeeeeeerrrrrrreeeeeeeeeee 22 Figure 3-6 Single shot detector Network Process ws2) Figure 3-7 License plate detected after evaluate Iéodel.---«e«««««< «2È Figure 3-8 Evaluate result dete€fion.c«eesc-secerkeeieieerrirriiiiirrrrro 2D) Figure 3-9 Stag 2: License Plate Recognition Proce sss27 Figure 3-10 TensorFlow to TensorFlow Lite conversion Architecture.
28 Figure 3-11 Scan activity with CaImer@X.uue 2D Figure 3-12 Cropping image process. Figure 3-13 Gray-scale image process. Figure 3-14 Result of license plate recognition cece3/2 Figure 3-15 Best case with correct recognition reSuÏf. 3⁄4 Figure 3-16 Worst case incorrect recognition F€SHÏI.
OD Figure 4-1 System Architecture wccessscssssssssssessesssesssessssnsessesessesssnissssessssnsneesessnnns OO Figure 4-2 Parking-in session workflow diagram vse37 Figure 4-3 Parking-out session workflow diagraim. 3Ö Figure 4-4 Report lost card workflow diagram usesOD Figure 4-5 Main use case diagram of Smart Tiny Parking Lot. 40 Figure 4-6 Manage Card Use case DqBTđI. «+ AO Figure 4-7 Manage Membership Use case Diagraim.ò--- 41 Figure 4-8 Manage Profile Use case DÌ4BTAIH.--ccccccseereriiiiririeerier 41 Figure 4-9 Manage Staff Use case Did gram crests 42 Figure 4-10 Manage Parking History Use case Didgram.
42 Figure 4-11 Entity Relationship Diagram vues 60 Figure 4-12 Scan license plate User Interface .---------eccceeeeceecrrrr 63 Figure 4-13 Detailed checkout information User Interface 64 Figure 4-14 Login User Interface.65 Figure 4-15 Register staff account User ÏnfeFƒA4C€.---------eeeeeeeeeeeerrr 66 Figure 4-16 Manage card User Interface wiccccscsssssssssescssssssessssssssnsnsiesssessssnniesess 67 Figure 4-17 Manage parking history User Ïnf€ï/ƒC€.ecccceceeecveeeeeeeerer 68 Figure 4-18 Manage staff User Tnter face .ceecssscssccscsssessssssesssssssssessessnsnsnessessessnnns 69 Figure 4-19 Manage profile User Interface .cccscssssssssssesorsssssesessssnsniisseessessssnnnsesieenss 70 Figure 4-20 Detailed information of each parking history User Interƒace. 71 Figure 4-21 Detailed parking history of report lost card User Interface. 72 Figure 4-22 Recovery lost card User Interface.sesesssssssssesssesssssssssesssssesssessssesssseesssessets 73 Figure 4-23 Filter parking history User Interface .-ee-eeecce> 74 Figure 4-24 Edit your profile User Interface occurrences 75 Figure 4-25 Change your password User Interface sess 76 Figure 4-26 Change password successfully notification User Interface. 77 Figure 4-27 Update profile successfully notification User Interface .78 Figure 4-28 Add new NFC card User Interface.79 LIST OF TABLES Table 3-1 Perforamnce evaluation of license plate recognifioni.
33 Table 4-1 Login Use case SpecifTcdfi0n. FA Table 4-2 Logout Use case Specification. 44 Table 4-3 Insert new card Use case Specification. 46 Table 4-4 Recovery lost card Use case Specification.
46 Table 4-5 Vehicle parking-in session Use case Specification ics+9 Table 4-6 Vehicle parking-out session Use case Specificafion. 0 Table 4-7 Filter parking history Use case Specification «0.D1 Table 4-8 Report lost card Use case Specification .e«eeceecereeceeeee DL Table 4-9 Change password staff Use case Specificafion. DD Table 4-10 Register new staff account Use case Specifïcafi0H. OA Table 4-11 Search staff Use case SpecificationrcsÐ Table 4-12 Change password Use case Specification wees90 Table 4-13 Edit profile Use case Specification ou.7 Table 4-14 Get membership information Use case Specification.
5Ö Table 4-15 Reset point Use case Specification .ssscsssicccccsessneesennneesennnn 29 Table 4-16 Database description for users table.61 Table 4-17 Database description for card table. -61 Table 4-18 Database description for customer table wienOZ Table 4-19 Database description for parking history table. O3 LIST OF ACRONYMS AND ABBREVIATIONS No. Acronyms Meaning 1 LPR License Plate Recognition 2 VLP Vietnamese License Plate 3 LPD License Plate Detection 4 ALPD Automatic License Plate Detection 5 ALPR Automatic License Plate Recognition 6 RFID Radio Frequency Identification 7 NFC Near Field Communication 8 VLP Vietnam License Plate 9 ID Identification 10 JVM Java Virtual Machine 11 MVC Model-View-Controller 12 P2P Pear to Pear 13 ECDSA Elliptic Curve Digital Signature 14 OCR Optical Character Recognition 15 ML Kit Machine Learning Kit 17 SSD MobileNet V2_ | Single Shot Detector Mobile Network version 2 18 FPS Frame per second ABSTRACT Derived from economic and social development, the number of vehicles increases leads to an increase in people's participation in traffic.
Smart traffic management systems are gradually appearing and developing, to reduce work for people. Many technology solutions in the field of transportation for vehicle management and monitoring, one of them is automatic license plate recognition technology. This technology helps to recognize license plates faster and more accurately by processing a photo of the vehicle's license plate. The tollbooth uses this system to recognize license plates to collect tolls for vehicles, CCTV system on highways to recognize speed and license plate information of moving vehicles to monitor traffic.
In recent years, the traffic of vehicles concentrated in public places is very large. Management of vehicles in public places and parking lots is a problem that needs to be solved. This system requires high-cost specialized equipment such as cameras, PC, barriers, and RFID/NFC card readers. Therefore, it is not feasible for cafes, restaurants, eateries, and convenience stores to use this smart parking system because of the installation area and operating costs.
Smartphones combine all components of the parking system, so the solution to this problem is to build a mobile application that integrates the license plate recognition algorithm. In this graduate thesis, I focused on detecting and recognizing Vietnamese license plates from images and videos in real-time based on a mobile application and NFC technology to store vehicle management history at parking lots. This application helps to solve the problem of mobility and flexibility for parking staff, and area and saves installation and operating costs, with high accuracy. Along with that, I use customer information to visit the store to support the ERP, CRM system for managing the membership reward of that store.
Rationale The license plate is an identifier for each vehicle. Current popular solutions for managing vehicles, parking lot guard will write the license plate on the parking pass, then they will give you the parking pass. The disadvantage of paper parking card is that it is easy to lose, get wet, or tear, and it is not difficult for bad guys to fake it. License plate recognition is reading and analyzing, recognizing the content of the license plate to manage vehicle information.
And the fact that in terms of recognizing and checking license plates humans can make mistakes sometimes and can't do as well as machines, at least humans can only identify well in the number of vehicles. In the current digital age, it is important for a store to collect customer information to serve many purposes such as marketing, understanding satisfaction, and membership management. Stores often use the method of accumulating points with a member loyalty card containing personal information or by requesting a customer's phone number to accumulate points. However, personal information is easily collected and leaked by the development of technology.
So, customers are very concerned about the security of personal information and feel uncomfortable about sharing too much information for many stores. Our application uses the customer's parking information, each license plate, and a number of personal identifiers of each customer, combined with NFC cards such as membership cards to manage membership, make customers feel comfortable and surprised when they don't have to share information and still receive membership point. Therefore, the study of this problem is necessary. In my major is Information Systems, this problem has certain relevance in the system definition, the mobile application is a vehicle management system, and we can use the data source of the application to be integrated ERP/CRM system for managing the membership reward of that store.
Aims and Objectives: With this graduation thesis, the objectives that I aim for when doing this is as follows: e Applying NFC technology to create communication between an NFC tag and android-powered device to store and query data stored in NFC tag. e Learning about license plate recognition, how to apply TensorFlow Lite and OCR to solve that problem. e Understanding and applying the model to solve the problem of license plate recognition, thereby building an android application using this model and NFC technology to smart vehicle parking management application. Scope of study This course focuses on the study of the Automatic License Plate Recognition method.
The automatic license plate recognition system is a system capable of analyzing and processing images license plate on the images taken directly from the camera. The input to the system is a photo of a vehicle with a license plate taken directly from the smartphone's camera. The output is the character string on the number license plate. This Automatic License Plate Recognition method applies the knowledge of Machine Learning and TensorFlow Lite tool to create license plate detection machine learning model.
We also use Labellmg tool graphical image annotation to draw visual boxes around license plate in each image. With the application of this Automatic License Plate Recognition method to practical applications for tiny parking lot management.