Drones or other UAVs provide an effective method to collect high resolution aerial images of the vegetation in large regions in the natural landscape. To analyse the collected images, object detection based on convolutional neural networks (CNNs) yields a highly accurate method to gather information about the recorded environment. The goal of this thesis is to develop a CNN-based object detector for annotating plants (e.g. conifer seedlings) or general objects (e.g. debris) on a given corpus of aerial images. For this project, we experiment with advanced object detectors loke faster RCNN and examine transfer learning techniques to reduce the influence of seasons.
This project contributes to the Boreal Ecosystem Recovery and Assessment (BERA) Project and is a collaboration within the Bavaria-Alberta research network Abby-Net.
- Michael Fromm
- Matthias Schubert
- Evgeniy Faerman
- Michael Fromm, Matthias Schubert, Guillermo Castilla, Julia Linke and Greg McDermid (2019):Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks, Remote Sensensing 11(21), 2585; https://doi.org/10.3390/rs11212585
- Evgeniy Faerman, Manuel Rogalla, Niklas Strauß, Adrian Krüger, Benedict Blümel, Max Berrendorf, Michael Fromm, and Matthias Schubert (2019), XD-STOD: Cross-Domain Superresolution for Tiny Object Detection, at 1st IEEE ICDM Workshop on Deep Learning for Spatiotemporal Data, Algorithms, and Systems (DeepSpatial 2019)