arXiv subjects

Matthew Dunbabin

Publications and source records attributed to Matthew Dunbabin.

3 recordsLinked to original sources

arXiv2025

Automated Coral Spawn Monitoring for Reef Restoration: The Coral Spawn and Larvae Imaging Camera System (CSLICS)

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging Camera System (CSLICS), which uses low cost modular cameras and object detectors trained using human-in-the-loop labeling approaches for automated spawn counting in larval rearing tanks. This paper details the system engineering, dataset collection, and computer vision techniques to detect, classify and count coral spawn. Experimental results from mass spawning events demonstrate an F1 score of 82.4% for surface spawn detection at different embryogenesis stages, 65.3% F1 score for sub-surface spawn detection, and a saving of 5,720 hours of labor per spawning event compared to manual sampling methods at the same frequency. Comparison of manual counts with CSLICS monitoring during a mass coral spawning event on the Great Barrier Reef demonstrates CSLICS' accurate measurement of fertilization success and sub-surface spawn counts. These findings enhance the coral aquaculture process and enable upscaling of coral reef restoration efforts to address climate change threats facing ecosystems like the Great Barrier Reef.

cs.RO↗

arXiv2022

Reconfigurable Robots for Scaling Reef Restoration

Coral reefs are under increasing threat from the impacts of climate change. Whilst current restoration approaches are effective, they require significant human involvement and equipment, and have limited deployment scale. Harvesting wild coral spawn from mass spawning events, rearing them to the larval stage and releasing the larvae onto degraded reefs is an emerging solution for reef restoration known as coral reseeding. This paper presents a reconfigurable autonomous surface vehicle system that can eliminate risky diving, cover greater areas with coral larvae, has a sensory suite for additional data measurement, and requires minimal non-technical expert training. A key feature is an on-board real-time benthic substrate classification model that predicts when to release larvae to increase settlement rate and ultimately, survivability. The presented robot design is reconfigurable, light weight, scalable, and easy to transport. Results from restoration deployments at Lizard Island demonstrate improved coral larvae release onto appropriate coral substrate, while also achieving 21.8 times more area coverage compared to manual methods.

cs.RO↗

arXiv2020

Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation

This paper presents a novel approach to underwater terrain mapping for Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex 3D environments. The proposed methodology creates a probabilistic elevation map of the terrain using a monocular image learning-based scene range estimator as a sensor. This scene range estimator can filter transient objects such as fish and lighting variations. The mapping approach considers uncertainty in both the estimated scene range and robot pose as the AUV moves through the environment. The resulting elevation map can be used for reactive path planning and obstacle avoidance to allow robotic systems to approach the underwater terrain as closely as possible. The performance of our approach is evaluated in a simulated underwater environment by comparing the reconstructed terrain to ground truth reference maps, as well as demonstrated using AUV field data collected within in a coral reef environment. The simulations and field results show that the proposed approach is feasible for obstacle detection and range estimation using a monocular camera in reef environments.

cs.RO↗