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.
Auteurs principaux: | Sanjana, Jasia, Al Muhit, Abdullah, Zia, Asma |
---|---|
Autres auteurs: | Rhaman, Dr. Md. Khalilur |
Format: | Thèse |
Langue: | English |
Publié: |
Brac University
2024
|
Sujets: | |
Accès en ligne: | http://hdl.handle.net/10361/22061 |
Documents similaires
-
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch
par: Uddin, Md. Minhaz, et autres
Publié: (2023) -
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments
par: Mahmud, Ishtiaque, et autres
Publié: (2024) -
Fire and disaster detection with multimodal quadcopter By machine learning
par: Afrin, Anika, et autres
Publié: (2023) -
Occluded object detection for autonomous vehicles employing YOLOv5, YOLOX and Faster R-CNN
par: Mostafa, Tanzim, et autres
Publié: (2022) -
Leveraging robust CNN architectures for real-time object recognition from conveyor belt
par: Moon, Nowrin Tasnim, et autres
Publié: (2023)