Veröffentlichung von "Comparative Analysis of YOLOv9, YOLOv10, and RT-DETR for Real-Time Weed Detection" / Publication of "Comparative Analysis of YOLOv9, YOLOv10, and RT-DETR for Real-Time Weed Detection" [28.10.24]
Paper auf dem 9. Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at European Conference on Computer Vision (ECCV) 2024 unter der Beteiligung von Ahmet Saltik und Fachgebietsleiter JProf. Anthony Stein erschienen. - Paper published at the 9th Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at European Conference on Computer Vision (ECCV) 2024 with the participation of Ahmet Saltik and our head of department JProf. Anthony Stein.
ENGLISH VERSION BELOW.
Ein weiteres Paper ist aus der Arbeit des KI-Fachgebiets erschienen.
Hier kommen die Hard-Facts zur Veröffentlichung.
Titel:
Comparative Analysis of YOLOv9, YOLOv10, and RT-DETR for Real-Time Weed Detection
Autoren:
A. Saltik, A. Allmendinger and A. Stein
Venue:
9th Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at European Conference on Computer Vision (ECCV) 2024
Beschreibung:
This study presents a comparative analysis of the state-of-the-art object detection models including YOLOv9, YOLOv10, and RT-DETR for real-time weed detection. The research focuses on achieving an optimal balance between model accuracy and inference time to support the deployment of deep learning based embedded systems in agriculture.
Unter folgendem Link kommen Sie zur gesamten Übersicht der bisherig veröffentlichten Forschungsarbeiten des Fachgebiets: ki-agrartechnik.uni-hohenheim.de/veroeffentlichungen
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Another paper has been published with the participation of the AI department.
Here are the hard facts about the publication.
Title:
Comparative Analysis of YOLOv9, YOLOv10, and RT-DETR for Real-Time Weed Detection
Authors:
A. Saltik, A. Allmendinger and A. Stein
Venue:
9th Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at European Conference on Computer Vision (ECCV) 2024
Summary:
This study presents a comparative analysis of the state-of-the-art object detection models including YOLOv9, YOLOv10, and RT-DETR for real-time weed detection. The research focuses on achieving an optimal balance between model accuracy and inference time to support the deployment of deep learning based embedded systems in agriculture.
The following link will take you to the complete overview of the research work published to date by the department: ki-agrartechnik.uni-hohenheim.de/veroeffentlichungen
