Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Manylion Llyfryddiaeth
Prif Awduron:	Sanjana, Jasia, Al Muhit, Abdullah, Zia, Asma
Awduron Eraill:	Rhaman, Dr. Md. Khalilur
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	Brac University 2024
Pynciau:	Primary dataset Data analysis Computer vision Object detection algorithm Machine learning YOLOv5. Signal detection.
Mynediad Ar-lein:	http://hdl.handle.net/10361/22061

id	10361-22061
record_format	dspace
spelling	10361-220612024-01-03T21:02:35Z Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck Sanjana, Jasia Al Muhit, Abdullah Zia, Asma Rhaman, Dr. Md. Khalilur Mukta, Jannatun Noor Department of Computer Science and Engineering, Brac University Primary dataset Data analysis Computer vision Object detection algorithm Machine learning YOLOv5. Signal detection. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 56-59). In order to reduce manpower and bottleneck in the inspection system of industrial garments, we explored the application of the YOLOv5 model using our very own dataset of defective clothing pieces. The economy of Bangladesh heavily depends on the garment industry. However, in this day and age of advanced technology, it is getting harder to have efficient manpower in the garment industry. Motivated to solve this problem, we decided to devise ways to explore AI implementations, particularly in the Bangladeshi garment industry system. Since there is no existing and efficient defective garment dataset specifically for our desired research work so we created our own dataset. This dataset has a total of 2,525 images and 7 different classes. By thoroughly analyzing our data from pre-processing to its performance after the application in the YOLOv5 model, we have tried to create a useful dataset. The models have achieved a good mean average precision across all 7 classes. Our research has only scratched a small surface of an area of interest where advanced AI and machine learning technologies can bring a lot more advancement. Jasia Sanjana Abdullah Al Muhit Asma Zia B.Sc. in Computer Science 2024-01-03T08:22:33Z 2024-01-03T08:22:33Z 2023 2023-08 Thesis ID: 22141069 ID: 18201024 ID: 18101540 http://hdl.handle.net/10361/22061 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 59 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Primary dataset Data analysis Computer vision Object detection algorithm Machine learning YOLOv5. Signal detection.
spellingShingle	Primary dataset Data analysis Computer vision Object detection algorithm Machine learning YOLOv5. Signal detection. Sanjana, Jasia Al Muhit, Abdullah Zia, Asma Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Rhaman, Dr. Md. Khalilur
author_facet	Rhaman, Dr. Md. Khalilur Sanjana, Jasia Al Muhit, Abdullah Zia, Asma
format	Thesis
author	Sanjana, Jasia Al Muhit, Abdullah Zia, Asma
author_sort	Sanjana, Jasia
title	Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
title_short	Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
title_full	Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
title_fullStr	Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
title_full_unstemmed	Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
title_sort	introducing ai in garment fault detection using yolov5 to reduce bottleneck
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22061
work_keys_str_mv	AT sanjanajasia introducingaiingarmentfaultdetectionusingyolov5toreducebottleneck AT almuhitabdullah introducingaiingarmentfaultdetectionusingyolov5toreducebottleneck AT ziaasma introducingaiingarmentfaultdetectionusingyolov5toreducebottleneck
_version_	1814306846961303552

Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck

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