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.
Główni autorzy: | Sanjana, Jasia, Al Muhit, Abdullah, Zia, Asma |
---|---|
Kolejni autorzy: | Rhaman, Dr. Md. Khalilur |
Format: | Praca dyplomowa |
Język: | English |
Wydane: |
Brac University
2024
|
Hasła przedmiotowe: | |
Dostęp online: | http://hdl.handle.net/10361/22061 |
Podobne zapisy
-
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch
od: Uddin, Md. Minhaz, i wsp.
Wydane: (2023) -
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments
od: Mahmud, Ishtiaque, i wsp.
Wydane: (2024) -
Fire and disaster detection with multimodal quadcopter By machine learning
od: Afrin, Anika, i wsp.
Wydane: (2023) -
Occluded object detection for autonomous vehicles employing YOLOv5, YOLOX and Faster R-CNN
od: Mostafa, Tanzim, i wsp.
Wydane: (2022) -
Leveraging robust CNN architectures for real-time object recognition from conveyor belt
od: Moon, Nowrin Tasnim, i wsp.
Wydane: (2023)