Titel

Merkliste
1 Ergebnisse

Disease detection by YOLOv2, Tiny-YOLO, and DF-Tiny-YOLO:

Jie Di (756926) ; Qing Li (84975)
https://figshare.com/articles/figure/Disease_detection_by_YOLOv2_Tiny-YOLO_and_DF-Tiny-YOLO_/19104227. , 2022

Link: https://doi.org/10.1371/journal.pone.0262629.g007

RT Journal
T1 <Title>Disease detection by YOLOv2, Tiny-YOLO, and DF-Tiny-YOLO</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_19104227&Exemplar=1&LAN=DE
				
A1 Jie Di (756926)
A1 Qing Li (84975)
YR 2022
K1 Molecular Biology
K1 Space Science
K1 Biological Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 yolov2 network models
K1 perplexed orchard farmers
K1 paper significantly improves
K1 judging similar diseases
K1 apple leaf veins
K1 apple leaf diseases
K1 88 %
K1 respectively
K1 allow feature stacking
K1 achieve feature fusion
K1 operating detection speed
K1 effective automatic detection
K1 mean average precision
K1 3 &# 215
K1 1 &# 215
K1 investigated deep learning
K1 improving detection accuracy
K1 new method proposed
K1 detection accuracy
K1 deep learning
K1 feature reuse
K1 detection performance
K1 average intersection
K1 deep gradient
K1 several years
K1 results showed
K1 numerous studies
K1 method evaluation
K1 introduced resize
K1 including 1
K1 dimensionality reduction
K1 data modeling
K1 calculation parameters
K1 404 images
JF https://figshare.com/articles/figure/Disease_detection_by_YOLOv2_Tiny-YOLO_and_DF-Tiny-YOLO_/19104227
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0262629.g007
DO https://doi.org/10.1371/journal.pone.0262629.g007
SF ELIB - SuUB Bremen

Export

RefWorks (nur Desktop-Version!)
Flow (Zuerst in Flow einloggen, dann importieren)