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Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Overview of attention for article published in EURASIP Journal on Image and Video Processing, April 2023
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1 X user

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6 Mendeley
Title
Vehicle logo detection using an IoAverage loss on dataset VLD100K-61
Published in
EURASIP Journal on Image and Video Processing, April 2023
DOI 10.1186/s13640-023-00604-1
Authors

Xiaohui Shi, Shengli Ma, Yang Shen, Yankun Yang, Zexin Tan

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 33%
Researcher 1 17%
Unknown 3 50%
Readers by discipline Count As %
Engineering 1 17%
Unknown 5 83%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2023.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from EURASIP Journal on Image and Video Processing
#202
of 234 outputs
Outputs of similar age
#358,208
of 420,739 outputs
Outputs of similar age from EURASIP Journal on Image and Video Processing
#1
of 1 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 234 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them