Synthetic Dataset Generation and Adaptation for Human Detection
Abstract
In this report, we introduce an effort to use synthetic imagery to test machine learning classifiers as an alternative to large-scale unmanned aerial vehicle based data collections. The benefits of synthetic data are that it allows for the generation of a highly diverse dataset, gives you the ability to finely control experiments, and provides automatic annotation. We have demonstrated that synthetic data can be generated using a game engine and then used to perform experiments to validate machine learning classifiers. In these experiments, we visualized the expected performance of three classifiers Tiny-YOLO, YOLOv3, and RetinaNetand compared the performance of the classifiers to one another.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 13, 2020
- Accession Number
- AD1115446
Entities
People
- Damon Conover
- Eung Joo Lee
- Heesung Kwon
- Jie Yan
Organizations
- United States Army Research Laboratory