Đồ án tốt nghiệp - Design of passenger seat belt monitoring system

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND
EDUCATION FACULTY OF INTERNATIONAL EDUCATION

GRADUATION PROJECT

DESIGN OF PASSENGER SEAT BELT
MONITORING SYSTEM

LE MINH NHAT
Student ID 20119183
HOANG NGOC HUNG
Student ID : 20119133
Major: COMPUTER ENGINEERING TECHNOLOGY
Advisor: TRUONG NGOC SON, Assoc. Prof.

Ho Chi Minh City, Nov 2024

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness

-Ho Chi Minh City, Dec 20th, 2024

GRADUATION PROJECT ASSIGNMENT

Student name: Le Minh Nhat

Student ID: 20119183

Student name: Hoang Ngoc Hung

Student ID: 20119133

Advisor: Truong Ngoc Son, Assoc. Prof.

Phone number: 0931 085 929

Date of assignment:

Date of submission:

1.
Project title: DESIGN OF PASSENGER SEAT BELT MONITORING
SYSTEM.
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)

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness

-Ho Chi Minh City, Dec 20th, 2024

ADVISOR’S EVALUATION SHEET
Student name: LE MINH NHAT

Student ID: 20119183

Student name: HOANG NGOC HUNG

Student ID: 20119133

Major: Computer Engineering Technology
Project title: DESIGN OF A PASSENGER SEAT BELT MONITORING SYSTEM.
Advisor: Truong Ngoc Son, Assoc. Prof
EVALUATION
1. Content of the project:
.............................................................................................................................................
.............................................................................................................................................
.............................................................................................................................................
2. Strengths:
.............................................................................................................................................
.............................................................................................................................................
.............................................................................................................................................
3. Weaknesses:
.............................................................................................................................................
.................................................................................................
...

...
ainment companies and amusement park
operators to test the system in real-world scenarios, gathering feedback
to optimize design and performance.
55

•

Connect the system with other management platforms, such as ticket
tracking, operation scheduling, or participant tracking systems, to
improve efficiency and usability.

These enhancements will ensure the system is not only technically robust but
also user-friendly and adaptable to various conditions and environments. By
addressing these areas, the system can evolve into a comprehensive safety solution
suitable for both entertainment and transportation sectors.

56

REFERENCES

[1] IAAPA, “Amusement Ride Safety and Trends,” 2023.
[2] U.S. Consumer Product Safety Commission (CPSC), “Amusement Ride
Incident Reports,” 2023.
[3] Redmon, J., et al., “You Only Look Once: Unified, Real-Time Object
Detection,” CVPR 2016.
[4] Ultralytics, “YOLOv8 Documentation,” Ultralytics Official Website, 2023
[5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no.
7553, pp. 436–444, 2015.
[6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA:
MIT Press, 2016.
[7] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once:
Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
[8] J. Li, X. Zhang, and Y. Wang, “A Comprehensive Review of YOLO
Architectures in Computer Vision,” MDPI Sensors, vol. 21, no. 7, pp. 2504–4990,
2021.
[9] Lin, T.Y. Dollár, P. Girshick, R. He, K. Hariharan, B. Belongie, S. Feature
pyramid networks for object detection. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017
pp. 2117–2125.
[10] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once:
Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
57

[11] Z. Tian, D. Shen, H. Chen, and J. Li, “FCOS: Fully Convolutional One-Stage
Object Detection,” Proceedings of the IEEE International Conference on Computer
Vision (ICCV), 2019, pp. 9626–9635.
[12] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,”
Proceedings of the International Conference on Learning Representations (ICLR),
2014.
[13] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv
preprint arXiv:1804.02767, 2018.

58