Use AI to detect defect pin in electrical connector
This project aims to develop an intelligent inspection model capable of detecting defects in small electrical connector pins, which are critical components in many electronic systems. The work is structured into two primary components: data preparation and model development. In the data preparation...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
|
| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6206/1/fyp_CS_2025_YTZ.pdf http://eprints.utar.edu.my/6206/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1848452702160289792 |
|---|---|
| author | Yong, Tian Ze |
| author_facet | Yong, Tian Ze |
| author_sort | Yong, Tian Ze |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This project aims to develop an intelligent inspection model capable of detecting defects in small electrical connector pins, which are critical components in many electronic systems. The work is structured into two primary components: data preparation and model development. In the data preparation phase, a custom dataset will be generated, featuring images of electrical connectors with three common types of pin defects: missing, shifted, and rotated pins. High-quality image data is essential for accurate model training and reliable detection outcomes. The model development phase leverages the YOLOv8 object detection algorithm, selected for its balance of speed and accuracy in real-time applications. Image processing techniques are employed to enhance dataset quality, and the dataset is annotated manually to ensure precision in model training. Performance evaluation will be conducted using several key metrics—accuracy, recall, precision, and F1 score—to assess the model's capability in identifying defective pins effectively. This project ultimately seeks to offer a practical and automated solution for improving quality control in electrical connector manufacturing processes, reducing the need for manual inspection and minimizing human error. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6206 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.62062025-11-05T13:53:06Z Use AI to detect defect pin in electrical connector Yong, Tian Ze T Technology (General) TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering This project aims to develop an intelligent inspection model capable of detecting defects in small electrical connector pins, which are critical components in many electronic systems. The work is structured into two primary components: data preparation and model development. In the data preparation phase, a custom dataset will be generated, featuring images of electrical connectors with three common types of pin defects: missing, shifted, and rotated pins. High-quality image data is essential for accurate model training and reliable detection outcomes. The model development phase leverages the YOLOv8 object detection algorithm, selected for its balance of speed and accuracy in real-time applications. Image processing techniques are employed to enhance dataset quality, and the dataset is annotated manually to ensure precision in model training. Performance evaluation will be conducted using several key metrics—accuracy, recall, precision, and F1 score—to assess the model's capability in identifying defective pins effectively. This project ultimately seeks to offer a practical and automated solution for improving quality control in electrical connector manufacturing processes, reducing the need for manual inspection and minimizing human error. 2025-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6206/1/fyp_CS_2025_YTZ.pdf Yong, Tian Ze (2025) Use AI to detect defect pin in electrical connector. Final Year Project, UTAR. http://eprints.utar.edu.my/6206/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering Yong, Tian Ze Use AI to detect defect pin in electrical connector |
| title | Use AI to detect defect pin in electrical connector |
| title_full | Use AI to detect defect pin in electrical connector |
| title_fullStr | Use AI to detect defect pin in electrical connector |
| title_full_unstemmed | Use AI to detect defect pin in electrical connector |
| title_short | Use AI to detect defect pin in electrical connector |
| title_sort | use ai to detect defect pin in electrical connector |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utar.edu.my/6206/1/fyp_CS_2025_YTZ.pdf http://eprints.utar.edu.my/6206/ |
| url_provider | http://eprints.utar.edu.my |