YOLO-Based Techniques for the Crown-of-Thorns Starfish Detection: A Comparative Study

Crown-of-thorns Starfish (COTS) outbreaks are the major direct contributors to decline in coral cover. This research investigates the application of transfer learning and YOLO variants (YOLOv5 and YOLOR) for real-time detection of Crown-of-Thorns starfish (COTS) in underwater images. Due to the scarcity of COTS-specific datasets, the potential of leveraging pre-trained models and data augmentation techniques were explored. Our findings show that models trained on close-up COTS images achieved higher accuracy compared to those trained on wider reef scenes. While YOLOv5 performed better in terms of speed and accuracy, both models struggled to detect small objects and distinguish COTS from similar marine life. To address these limitations, future research should focus on data augmentation, curriculum learning, and multi-stage detection pipelines to enhance model generalization and robustness for real-world deployment.