4 citations · 9 across the 6 of their papers we have counts for
6 papers
YOLOv4: A Breakthrough in Real-Time Object Detection
Athulya Sundaresan Geetha
YOLOv4 achieved the best performance on the COCO dataset by combining advanced techniques for regression (bounding box positioning) and classification (object class identification)…
Performance of YOLOv7 in Kitchen Safety While Handling Knife
Athulya Sundaresan Geetha
Safe knife practices in the kitchen significantly reduce the risk of cuts, injuries, and serious accidents during food preparation. Using YOLOv7, an advanced object detection model…
What is YOLOv6? A Deep Insight into the Object Detection Model
Athulya Sundaresan Geetha
This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements co…
Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis
Athulya Sundaresan Geetha
Vehicle detection is an important task in the management of traffic and automatic vehicles. This study provides a comparative analysis of five YOLOv5 variants, YOLOv5n6s, YOLOv5s6s…
From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model
Athulya Sundaresan Geetha, Muhammad Hussain
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 image…
A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety
Athulya Sundaresan Geetha, Muhammad Hussain
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 compar…