Rhaman, Dr. Md. KhalilurUddin, Md. MinhazFoysal, Sadi MahmudRahman, SadiaRisti, Nushara TazrinSarmin, Sanzeda Akter2023-08-082023-08-0820232023-01ID: 19101013ID: 22241046ID: 19141001ID: 22241041ID: 19101026http://hdl.handle.net/10361/19356Cataloged from PDF version of thesis.Includes bibliographical references (pages 43-44).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.In the era of computer vision to overcome challenges, the introduction of the YOLO model revolutionized real-time computer vision approaches. In the garment industry, the inception of products plays a significant role while increasing the processing time with a good accuracy rate is the big challenge here. A real-time garments defect detection approach using YOLOv7, YOLOv7x, and YOLOv7-w6 on a primary dataset is proposed with a good FPS rate and better accuracy. Maximum traditional garments inception approaches focused on end product defects while this model suggests detecting defects on the sewing phase so that the cost of the rejected end product can be optimized by detecting them before a product goes through all the phases. For this our research is more focused on three subclasses of Seam, Stitch, and Hole related to sewing phase defects. To increase the detection rate, the hyperparameter tuning technique is applied to the YOLOv7 model. Three models are proposed based on pre-trained weights of YOLOv7, YOLOv7x, and YOLOv7- w6 to compare the accuracy and FPS rate in terms of implementation in real-world projects.44 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.TILDAYOLOv7YOLOv7xYOLOv7-W6 CNNRCNNDCNNFaster-RCNNNeural NetworksReal-TimeComputer visionFaultDefectDetectionGarmentsHoleStitchSeamRoboflowSignal detection.Thesis