Master of Philosophy
School of Computing and Information Technology
Visual object detection has made significant progress with the advent of deep neural networks and has been extensively applied. This thesis reports a novel application that aims to detect individual microatolls, which are circular coral colonies, from island images captured by drones. We first describe data collection and labelling to create a novel microatoll dataset for the microatoll detection task from drone images. Upon this dataset, the state-of-the-art object detectors are then evaluated for this task. To better integrate a detector with the characteristic of microatolls, we propose a modified detector called Microatoll-Net. It actively extracts features from the surrounding area of a microatoll to differentiate it from distractors to improve detection. Multiple ways to incorporate this information into the detector are designed. The experimental study shows the efficacy of the proposed Microatoll-Net, especially on the most challenging area for detection. Besides, in geographical research, the position of a microatoll is more important than its size. It means that we shall pay more attention to detecting the centre of a microatoll instead of generating its bounding box. Motivated by this, we transform this object detection task into an object centre detection task.
Zhou, Zhexuan, Deep Learning Based Microatoll Detection From Drone Images, Master of Philosophy thesis, School of Computing and Information Technology, University of Wollongong, 2021. https://ro.uow.edu.au/theses1/1204
FoR codes (2008)
0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.