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

Hui-Hui Chen, Chiao-Wen Kao, B. Hwang, Kuo-Chin Fan
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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.
基于YOLO的深度学习网络识别和分级3D建模对象
本研究提出了一种新的方法,利用基于YOLO的深度学习网络来帮助教师自动对学习者创建的3D建模对象进行评分。训练数据集是教师的3D建模对象的渲染输出的集合。测试数据是学习者项目的呈现输出。评分将依赖于识别置信度的测试结果。这是深度学习网络对目标检测和识别的初步研究。更多的应用和修改将在进一步的研究中进行讨论、设计和检验。
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