Collecting and Using a Labeled Dataset of NATO Mission Task Symbols to Improve and Benchmark Detection Models
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Neural networks are commonly used for object detection tasks but require immense
amounts of data to train. For the task of North Atlantic Treaty Organization (NATO)
mission task symbol detection using object detection neural networks, it is not possible
to meet the data requirements. Additionally, labeling mission task symbols is very
time-consuming and costly.
This thesis aims to collect and label a dataset of NATO mission task symbols, propose
a part of it as a benchmark for our solutions and future solutions, and finally propose
different methods to use a part of the scarce collected data to improve the performance of
our object detection models. YOLOv5 neural network is selected and used to experiment
with different ways of using the scarce collected data. As a result, 113 images were
collected and labeled. Five performance metrics are proposed for the benchmark. Finally,
it was discovered that when dataset size is limited, extracting information from the
dataset and using it to generate artificial data improves performance compared to directly
introducing the scarce dataset to symbol detection models.
Description
Keywords
Machine learning, deep learning, computer vision, object detection, symbol detection, image processing, benchmark