학술논문
Training Artificial Intelligence Algorithms with Automatically Labelled UAV Data from Physics-Based Simulation Software
Document Type
article
Author
Source
Applied Sciences, Vol 13, Iss 1, p 131 (2022)
Subject
Language
English
ISSN
13010131
2076-3417
2076-3417
Abstract
Machine-learning (ML) requires human-labeled “truth” data to train and test. Acquiring and labeling this data can often be the most time-consuming and expensive part of developing trained models of convolutional neural networks (CNN). In this work, we show that an automated workflow using automatically labeled synthetic data can be used to drastically reduce the time and effort required to train a machine learning algorithm for detecting buildings in aerial imagery acquired with low-flying unmanned aerial vehicles. The MSU Autonomous Vehicle Simulator (MAVS) was used in this work, and the process for integrating MAVS into an automated workflow is presented in this work, along with results for building detection with real and simulated images.