Localising objects with drones: A case study on the localisation of fisher boats in restricted areas

Master thesis (2023)

Authors

L.Y. Geers Architecture and the Built Environment

Contributors

B.M. Meijers Digital Technologies - Architecture and the Built Environment (mentor)
A. Rafiee Digital Technologies - Architecture and the Built Environment (mentor)
E. Verbree Digital Technologies - Architecture and the Built Environment (graduation committee member)
Faculty
Architecture and the Built Environment, Architecture and the Built Environment
Copyright: © 2023 Lisa Geers
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Published Date

17-01-2023

Language

English

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Faculty

Architecture and the Built Environment

Abstract

Due to safety or preservation reasons, certain objects or areas need to be inspected regularly. Currently, the inspection of objects is mostly done in-person, which is labour-intensive and not very effective. With the use of drones, areas and objects can be inspected from new angles at a much faster rate. To effectively monitor these objects with drones, the position and location needs to be extracted from drone data in real time.
In this thesis, a case study is done on the localisation of fisher boats in restricted areas. Several components are integrated to create a prototype. The pretrained YOLOv3 detection model is trained on acquired nadir boat images, which makes it able to predict the bounding boxes of boats on images captured with drones. A positioning algorithm is constructed, which calculates the geographical coordinates from the pixel coordinates for images taken both in a nadir and an oblique angle. A real time connection is constructed between the drone and the prototype. This is done by creating a connection with Google Drive with the drone controller and the prototype. The positioned polygon bounding boxes are localised using a real time dashboard, which visualises the bounding boxes in a map with other relevant layers.
The results indicate that the performance of the components and prototype as a whole are satisfactory for this use case. To deploy this prototype in other object localisation use cases, it is recommended to train the pretrained model further, use a drone with more accurate equipment and run the prototype on the drone controller.