Sea turtle species identification is vital for marine biodiversity conservation, as sea turtles impact marine ecosystem balance by consuming dead seagrass and maintaining coral reefs. They help preserve the health of seagrass beds and coral reefs that benefit commercially valuable species. Therefore, to sustain sea turtle populations, detection systems that facilitate conservation efforts are essential. In developing underwater detection models, researchers must address several challenges specific to the underwater environment, including low illumination conditions, complex backgrounds, and underwater blur effects. In addition, YOLOv10-nano has emerged as the most efficient object detector in its family, though improving its performance remains a challenge. To overcome this issue, we propose an advanced deep learning approach using modified YOLOv10-nano with a new Parallel Fusion Module (PFM) integrated into the backbone alongside self-attention to enhance detection performance, named TurtleNet. The Parallel Fusion Module enhances detection performance by capturing channel-wise representational features. It emphasizes channels with relevant information through a dual-scaling process, improving feature quality. PFM is integrated into the untouched branch of the Partial Self-Attention mechanism to enrich the split half of the feature channels. Our model uses 48,302 images from Bunaken National Marine Park containing Green, Hawksbill, and Olive Ridley turtles with data augmentation applied. The method leverages YOLOv10-nano's real-time detection capabilities while the PFM optimizes feature fusion and localization accuracy. Experimental results show our model achieves an mAP50 score of 0.856 and runs at 28 FPS on CPU devices, outperforming existing approaches in precision, recall, and efficiency. This research combines computer vision with marine biology, creating an automated system that helps researchers and conservationists monitor endangered turtles.
Prasetya et al. (Wed,) studied this question.