Rhaman, Dr. Md. KhalilurMukta, Jannatun NoorSanjana, JasiaAl Muhit, AbdullahZia, Asma2024-01-032024-01-0320232023-08ID: 22141069ID: 18201024ID: 18101540http://hdl.handle.net/10361/22061Cataloged from PDF version of thesis.Includes bibliographical references (pages 56-59).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.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.59 pagesenBrac 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.Primary datasetData analysisComputer visionObject detection algorithmMachine learningYOLOv5.Signal detection.Thesis