Utilization of Data Augmentation Techniques to Enhance Learning with Sparse Datasets

被引：0

作者：

Yarnell, Richard ^{[1
]}

Brignac, Daniel ^{[2
]}

Fu, Yanjie ^{[3
]}

DeMara, Ronald F. ^{[1
]}

机构：

[1] Univ Cent Florida, Elect & Comp Engn, Orlando, FL 32816 USA

[2] Univ Cent Florida, Ctr Res Comp Vis, Orlando, FL 32816 USA

[3] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 USA

来源：

2022 5TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES, AI4I | 2022年

关键词：

Machine Learning; You Only Look Once (YOLO);

D O I：

10.1109/AI4I54798.2022.00025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Neural network-based object detection has many important applications but requires a vast amount of training data. In applications where training data may be scarce, data augmentation techniques can be used to expand the training set. This paper explores the performance of such techniques on You Only Look Once Version 5 (YOLOv5).

引用

页码：73 / 73

页数：1

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