Zobrazeno 1 - 10
of 6 330
pro vyhledávání: '"HUSSAIN, Muhammad"'
Autor:
Khanam, Rahima, Hussain, Muhammad
This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Pa
Externí odkaz:
http://arxiv.org/abs/2410.17725
The MS-GLMB filter offers a robust framework for tracking multiple objects through the use of multi-sensor data. Building on this, the MV-GLMB and MV-GLMB-AB filters enhance the MS-GLMB capabilities by employing cameras for 3D multi-sensor multi-obje
Externí odkaz:
http://arxiv.org/abs/2410.14977
Transformer-based models have transformed the landscape of natural language processing (NLP) and are increasingly applied to computer vision tasks with remarkable success. These models, renowned for their ability to capture long-range dependencies an
Externí odkaz:
http://arxiv.org/abs/2408.15178
The Segment Anything Model (SAM), introduced to the computer vision community by Meta in April 2023, is a groundbreaking tool that allows automated segmentation of objects in images based on prompts such as text, clicks, or bounding boxes. SAM excels
Externí odkaz:
http://arxiv.org/abs/2408.06305
Autor:
Khanam, Rahima, Hussain, Muhammad
This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explo
Externí odkaz:
http://arxiv.org/abs/2407.20892
Knife safety in the kitchen is essential for preventing accidents or injuries with an emphasis on proper handling, maintenance, and storage methods. This research presents a comparative analysis of three YOLO models, YOLOv5, YOLOv8, and YOLOv10, to d
Externí odkaz:
http://arxiv.org/abs/2407.20872
Autor:
Hussain, Muhammad
This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance improvements, and suitability for
Externí odkaz:
http://arxiv.org/abs/2407.02988
This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of agricultural advancements. The primary objective is to elucidate how these cutting-edge object detection mo
Externí odkaz:
http://arxiv.org/abs/2406.10139
Blockchain technology, heralded as a transformative innovation, has far-reaching implications beyond its initial application in cryptocurrencies. This study explores the potential of blockchain in enhancing data integrity and traceability within Indu
Externí odkaz:
http://arxiv.org/abs/2405.04837
Publikováno v:
The 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023)
We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence thresh
Externí odkaz:
http://arxiv.org/abs/2312.01650