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.
Hoofdauteurs: | Sanjana, Jasia, Al Muhit, Abdullah, Zia, Asma |
---|---|
Andere auteurs: | Rhaman, Dr. Md. Khalilur |
Formaat: | Thesis |
Taal: | English |
Gepubliceerd in: |
Brac University
2024
|
Onderwerpen: | |
Online toegang: | http://hdl.handle.net/10361/22061 |
Gelijkaardige items
-
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch
door: Uddin, Md. Minhaz, et al.
Gepubliceerd in: (2023) -
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments
door: Mahmud, Ishtiaque, et al.
Gepubliceerd in: (2024) -
Fire and disaster detection with multimodal quadcopter By machine learning
door: Afrin, Anika, et al.
Gepubliceerd in: (2023) -
Occluded object detection for autonomous vehicles employing YOLOv5, YOLOX and Faster R-CNN
door: Mostafa, Tanzim, et al.
Gepubliceerd in: (2022) -
Leveraging robust CNN architectures for real-time object recognition from conveyor belt
door: Moon, Nowrin Tasnim, et al.
Gepubliceerd in: (2023)