Đồ án tốt nghiệp - Detection of missing components on circuit board using image processing

HO CHI MINH CITY UNIVERSITY OF
TECHNOLOGY AND EDUCATION
FACULTY OF INTERNATIONAL
EDUCATION


GRADUATION THESIS
Topic: DETECTION OF MISSING COMPONENTS ON
CIRCUIT BOARD USING IMAGE PROCESSING

Advisor

:

PhD. Ta Van Phuong

Student’s name

:

Tra Minh Nguyen

Student ID

:

18151023

Ho Chi Minh, July, 2025

TABLE OF CONTENT
DISCLAIMER ...............................................................................................iv
TABLE OF CONTENT .................................................................................. v
TABLE OF FIGURE ....................................................................................vii
LIST OF TABLEs .........................................................................................ix
ABSTRACT.................................................................................................... x
Chapter 1 : INTRODUCTION ....................................................................... 1
1.1. Problem statement ................................................................................ 1
1.2. Aim of study......................................................................................... 1
1.3. Object and range of study .................................................................... 1
1.4. Research methods ................................................................................ 2
1.5. Structure of study ................................................................................. 2
Chapter 2 : THEORETICAL BASIS .............................................................. 3
2.1. Overview of Instance Segmentation by YOLOv11 ............................. 3
2.1.1. YOLOv11 Fundamentals .............................................................. 3
2.1.1.1. Introduction ............................................................................ 3
2.1.1.2. Features of YOLOv11 ............................................................ 3
2.1.1.3. YOLOv11 Architecture .......................................................... 6
2.1.2. Yolov11 Performance Analysis and Implementation ................. 14
2.1.2.1. Material and methods ........................................................... 14
2.1.2.2. Image collection ................................................................... 15
2.1.2.3. Data Processing .................................................................... 16
2.1.2.4. Annotation .............................................
...

...
m/2024/11/13/10/
[2] GeeksforGeeks, “What is YOLOv11?” GeeksforGeeks, [Online].
Available: https://www.geeksforgeeks.org/blogs/what-is-yolov11/
[3] Ultralytics, “YOLOv11 Overview,” Ultralytics Documentation, [Online].
Available: https://docs.ultralytics.com/models/yolo11/#overview
[4] A. Sharma, “YOLOv11 Model Building – From Scratch,” Analytics
Vidhya,
Jan.
2025.
[Online].
Available:
https://www.analyticsvidhya.com/blog/2025/01/yolov11-model-building/
[5] IBM, “Instance Segmentation,” IBM Think,[Online]. Available:
https://www.ibm.com/think/topics/instance-segmentation
[6] Albumentations, “What are Image Augmentations?” Albumentations
Documentation,[Online]. Available: https://albumentations.ai/docs/1introduction/what-are-image-augmentations/
[7] Basler AG, “ACA2500-14GC Product Documentation,” Basler Official
Documentation, [Online]. Available: https://docs.baslerweb.com/aca250014gc
[8] S. P. Chhetri, S. Bhat, P. Timalsina, and B. T. Magar, “Detection of
Missing Component in PCB Using YOLO,” International Journal on
Engineering Technology (InJET), vol. 1, no. 1, pp. 62–71, Nov. 2023. DOI:
10.3126/injet.v1i1.60902.
[9] N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “YOLO Evolution:
A Comprehensive and Architectural Review of YOLOv12,
YOLO11, and Their Previous Versions,” Image and Vision Computing,
preprint, Mar. 2025. [Online]. Available: https://arxiv.org/abs/2411.00201
[10] P. Hidayatullah, N. Syakrani, M. R. Sholahuddin, T. Gelar, and R.
Tubagus, “YOLOv8 to YOLO11: A Comprehensive Architecture In-depth
Comparative Review,” Preprint, Politeknik Negeri Bandung, Jan. 2025.
[11] R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key
Architectural Enhancements,” Department of Computer Science,
Huddersfield
University,
Oct.
2024.
[Online].
Available:
https://arxiv.org/abs/2410.17725.

Department of Automatic Control

56

CONCLUSION AND FUTURE WORKS

APPENDIX
Main program for training YOLO model

Main program of system

Department of Automatic Control

57

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

58

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

59

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

60

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

61

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

62

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

63

CONCLUSION AND FUTURE WORKS

Department of Automatic Control

64