Crown-of-Thorns Starfish Detection by state-of-the-art YOLOv5

Crown-of-Thorns Starfish outbreaks appeared many decades ago which have threatened the overall health of the coral reefs in Australia’s Great Barrier Reef. This indeed has a direct impact on the reef-associated marine organisms and severely damages the biological diversity and resilience of the habitat structure. Yet, COTS surveillance has been carried out for long but completely by human effort, which is absolutely ineffective and prone to errors. There emerges an urge to apply recent advanced technology to deploy unmanned underwater vehicles for detecting the target object and taking suitable actions accordingly. Existing challenges include but not limited to the scarcity of qualified underwater images as well as superior detection algorithms which is able to satisfy major criteria such as light-weight, high accuracy and speedy detection. There are not many papers in this specific area of research and they can’t fulfill these expectations completely. In this thesis, we propose a deep learning based model to automatically detect the COTS in order to prevent the outbreak and minimize coral mortality in the Reef. As such, we use CSIRO COTS Dataset of underwater images from the Swain Reefs region to train our model. Our goal is to recognize as many starfish as possible while keeping the accuracy high enough to ensure the reliability of the solution. We provide a comprehensive background of the problem, and an intensive literature review in this area of research. In addition, to better align with our task, we use F2 score as the main evaluation metrics in our MS COCO- based evaluation scheme. That is, an average F2 is computed from the results obtained at different IoU thresholds, from 0.3 to 0.8 with a step size of 0.05. In our implementation, we experiment with model architecture selection, online image augmentation, confidence score threshold calibration and hyperparameter tuning to improve the testing performance in the model inference stage. Eventually, we present our novel COTS detector as a promising solution for the stated challenge