HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND
EDUCATION
FACULTY FOR HIGH QUALITY TRAINING

GRADUATION PROJECT

DESIGN AND IMPLEMENTATION OF
CLASSFICATION AND DELIVERY BASED ON
COMPUTER VISION

PHẠM MINH QUÂN
Student ID: 18161031
NGUYỄN HOÀI PHƯƠNG UYÊN
Student ID: 18119053
Major: COMPUTER ENGINEERING TECHNOLOGY
Advisor: TRƯƠNG QUANG PHÚC, M.Eng

Ho Chi Minh City, December 2022

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND
EDUCATION
FACULTY FOR HIGH QUALITY TRAINING

GRADUATION PROJECT

DESIGN AND IMPLEMENTATION OF
CLASSIFICATION AND DELIVERY BASED ON
COMPUTER VISION

PHẠM MINH QUÂN
Student ID: 18161031
NGUYỄN HOÀI PHƯƠNG UYÊN
Student ID: 18119053
Major: COMPUTER ENGINEERING TECHNOLOGY
Advisor: TRƯƠNG QUANG PHÚC, M.Eng

Ho Chi Minh City, December 2022

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness



Ho Chi Minh City, December 25, 2022

GRADUATION PROJECT ASSIGNMENT
Student name: Phạm Minh Quân

Student ID: 18161031

Student name: Nguyễn Hoài Phương Uyên

Student ID: 18119053

Major: Computer Engineering Technology

Class: 18119CLA

Advisor: Trương Quang Phúc, MEng

Phone number: _________________

Date of assignment:

Date of submission: _____________

_____________________
1. Project title: Design and Implementation of classification and delivery based on Computer
Vision
2. Initial materials provided by the advisor: ___________________________________
3. Content of the project: _________________________________________________
4. Final product: ________________________________________________________

CHAIR OF THE PROGRAM

ADVISOR

(Sign with full name)

(Sign with full name)

Trương Quang Phúc

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness
-Ho Chi Minh City, December 25, 2022
ADVISOR’S EVALUATION SHEET
Student name: Phạm Minh Quân
Student name: Nguyễn Hoài Phương Uyên

Student ID: 18161031
Student ID: 18119053

Major: Computer Engineering Technology
Project title: Design and Implementation of classification and delivery based on Computer
Vision
Advisor: Trương Quang Phúc, MEng
EVALUATION
1. Content of the project:
................................................................................................................................................
................................................................................................................................................
2. Strengths:
..........................................................
...

...
Wi-Fi link
for package delivery to each fixed compartment.
• Additionally, the system is equipped with tracking software that indicates the
position of autonomous cars, the location of warehouses, and the amount of
packages on each warehouse.
Following this subject, team members will learn more about neural models that
enhance this models recognition capabilities. In addition, we will learn more about
hardware and mechanical design in order to construct a system that can be applied in
the real world.
5.2 Future Work
Based on the results obtained while implementing this project, we hope to develop
and upgrade the project in the future to be able to apply delivery in the warehouses of
manufacturing plants that use automation technology.
We will expand the model by making the vehicle larger in order to transport large,
heavy packages. In addition, we add self-sorting functionality to the warehouse floors
to reduce labor costs and production time.

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REFERENCE
[1] R. N. Keiron O’Shea, An Introduction to Convolutional Neural Networks, CoRR, p.
11, 2015.
[2] IndoML, Convolutional Neural Networks (CNN) Introduction, 7 March 2018.
[Online]. Available: https://indoml.com/2018/03/07/student-notes-convolutional-neuralnetworks-cnn-introduction/?fbclid=IwAR2Clu7-ZWgDygWwpS16XheKZ1kLhiT8eLglBQ-85QKjaEtmH9OOWysb6A.
[3] J. Redmon, You Only Look Once: Unified, Real-Time Object Detection, CVPR, p. 10,
2016.
[4] C.-Y. Wang, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time
object detectors, CVPR, p. 15, 2022.
[5] V. T. Hang, AUTOMATIC PARCEL SORTING SYSTEM WITH AI
TECHNOLOGY, TNU Journal of Science and Technology, pp. 127-134, 2022.
[6] M. pakdaman, A Line Follower Robot from design to Implementation: Technical
issues and problems, ICCAE, pp. 5-9, 2010.
[7] A. Pathak, Line Follower Robot for Industrial Manufacturing Process, International
Journal of Engineering Inventions, pp. 10-17, 2017.
[8] R. Smith, An Overview of the Tesseract OCR Engine, Ninth International Conference
on Document Analysis and Recognition (ICDAR 2007), pp. 1-5, 2007.
[9] S. Rath, Performance Comparison of YOLO Object Detection Models – An Intensive
Study, [Online]. Available: https://learnopencv.com/performance-comparison-of-yolomodels/?fbclid=IwAR3YUKptiMxwZocJmtI70m-qVEgPf3UvpMLF2KGPV4kEp6kECuzne9mUDE..
[10] SPI communication protocol, [Online]. Available: https://aticleworld.com/spicommunication-protocol/.

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