BT-YOLO: An attention-enhanced framework for monitoring bioluminescent algal blooms in coastal tourism zones

Monitoring of bioluminescent algae blooms, referred to in China as “Blue Tears”, is essential for managing ecological risks, ensuring public safety, and accommodating the growing tourist attention they attract in coastal regions. However, this task remains challenging due to the nocturnal nature of these events, limited visibility, the lack of real-time monitoring infrastructure, and the absence of publicly available training datasets. To address this, we present BT3.8k, a novel dataset comprising 3827 images extracted from 241 user-uploaded videos shared on social media platforms. These samples cover diverse lighting, sea states, and viewpoints. Annotations were generated using Roboflow and SAM2, and subsequently refined using a YOLOv11m-seg model. We applied augmentations including Gaussian blur, salt-and-pepper noise, flipping, rotation, hue, brightness, and contrast to enhance generalizability. Using this dataset, we trained BT-YOLO, a YOLOv11 framework enhanced with the Convolutional Block Attention Module (CBAM), designed for coastal Blue Tear monitoring. Among multiple models evaluated, BT-YOLO achieved the highest performance with a Dice score of 85%, IoU of 77%, AP@50 of 80%, and 90 FPS on GPU. This approach offers a scalable, real-time solution for BT monitoring, with potential impact across marine ecology, eco-tourism management, and environmental governance.