Advancing Automated Surveillance: Real-Time Detection of Crown-of-Thorns Starfish via YOLOv5 Deep Learning - Xu, Guokun; Xie, Ying; Luo, Yang; Yin, Yibo; Li, Zhengning; Wei, Zibu (Journal of Theory and Practice of Engineering Science, 2024)
The Great Barrier Reef faces significant threats from crown-of-thorns starfish (COTS), which consume coral polyps and contribute to reef degradation. Traditional methods for detecting these starfish are manual and labor-intensive, limiting their scalability and efficiency. This study proposes a real-time detection system using deep learning and computer vision to identify COTS in underwater video frames. We utilize the YOLOv5 model, known for its speed and accuracy in object detection tasks. Extensive data augmentation techniques are employed to handle the challenges of the underwater environment, such as varying lighting conditions and water turbidity. Additionally, we modify the YOLOv5 architecture to improve the detection of small objects like COTS, which often blend into the reef. To enhance detection consistency, we integrate a video object tracking system that maintains object continuity across frames, reducing false positives. Our approach demonstrated significant improvements in detection accuracy, achieving a Public Leaderboard score of 0.715, which places us in the top 2% of submissions. This highlights the potential of our method for scalable and effective monitoring of the Great Barrier Reef, contributing to conservation efforts by providing a tool for continuous and automated detection of harmful species like COTS.