Recognizing and Grading 3D Modeling Objects Using YOLO Based Deep Learning Network

Hui Hui Chen, Chiao Wen Kao, Bor Jiunn Hwang, Kuo Chin Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study proposes a novel approach using YOLO based deep learning network to help the teacher grading 3D modeling objects created by the learners automatically. The training dataset is the collections of rendering outputs from the teacher's 3D modeling object. The testing data is the rendering outputs of the learners' projects. The grading will rely on the testing results of recognition confidences. This is an initial study from draft inspiration by the deep learning network on object detections and recognitions. More applications and modifications are to be discussed, designed and examined in further studies.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
StatePublished - Jul 2019
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: 7 Jul 201910 Jul 2019

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/TerritoryJapan
CityKobe
Period7/07/1910/07/19

Keywords

  • Grading 3D modeling objects
  • Object recognition
  • YOLO

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