| Autor: |
Dominique De Munck, Bram Janssen, Wouter Dierckx, Jan Biesemans, Stephen Kempenaers, Samuel Oswald, Tim Deroose, Jens Verrydt, Dieter Meeus, Dries Raymaekers, Pieter-Jan Baeck |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IGARSS |
| DOI: |
10.1109/igarss47720.2021.9553061 |
| Popis: |
Unmanned aerial vehicles (UAVs) have become a popular and useful tool for aerial mapping in a variety of domains. A limitation to even further adoption is the lack of scalability in many current methods, which require considerable manual work to incorporate new data. Inconsistent collection and processing of data can additionally prevent later consolidation and comparison of data across numerous dates and sites. An automated image processing workflow has been developed to allow end-users to plan and execute UAV flights with the end goal of publishing consistent and reliable data products. An example use case focusing on object detection for plant phenotyping shows the effectiveness of this workflow. Here, it is demonstrated that by utilizing this workflow, an end user is capable of producing consistent quality training images and annotations in a standardised way, allowing for the fast production and inference of new application models. As more data is adopted into the workflow, these models can be further improved by leveraging the additional data, which can reduce future training requirements and allow for progressively faster and more robust inference. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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