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Procedia Computer Science
Article . 2020 . Peer-reviewed
License: CC BY NC ND
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Procedia Computer Science
Article
License: CC BY NC ND
Data sources: UnpayWall
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Automatic Construction of Dataset with Automatic Annotation for Object Detection

Authors: Naoki Watanabe 0002; Shinji Fukui 0001; Yuji Iwahori; Yoshitsugu Hayashi; Witsarut Achariyaviriya; Boonserm Kijsirikul;

Automatic Construction of Dataset with Automatic Annotation for Object Detection

Abstract

Abstract This paper proposes a method for the automatic construction of a dataset with annotation data for object detection. The accuracy of the object detection method depends on the dataset in general. The dataset for object detection needs many images with annotation data. Obtaining image data by manual operation takes a lot of costs. It also costs much that annotation data are made by manual annotation software. This paper tries to solve these problems to construct the image dataset for the object detection automatically. The proposed method uses a method to collect the image data automatically among images on the Web using Web image mining. A new method for making annotation data is also proposed. The Mask R-CNN is used for automatical annotation. The proposed approach constructs a dataset automatically without almost manual operation. It is confirmed that the proposed approach performs the automatic construction of the dataset with high accuracy.

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    5
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Average
gold