Estimation of Artificial Reef Pose Based on Deep Learning

Abstract

Artificial reefs are man-made structures submerged in the ocean, and the design of these structures plays a crucial role in determining their effectiveness. Precisely measuring the configuration of artificial reefs is vital for creating suitable habitats for marine organisms. This study presents a novel approach for automated detection of artificial reefs by recognizing their key features and key points. Two enhanced models, namely, YOLOv8n-PoseRFSA and YOLOv8n-PoseMSA, are introduced based on the YOLOv8n-Pose architecture. The YOLOv8n-PoseRFSA model exhibits a 2.3% increase in accuracy in pinpointing target key points compared to the baseline YOLOv8n-Pose model, showcasing notable enhancements in recall rate, mean average precision (mAP), and other evaluation metrics. In response to the demand for swift identification in mobile fishing scenarios, a YOLOv8n-PoseMSA model is proposed, leveraging MobileNetV3 to replace the backbone network structure. This model reduces the computational burden to 33% of the original model while preserving recognition accuracy and minimizing the accuracy drop. The methodology outlined in this research enables real-time monitoring of artificial reef deployments, allowing for the precise quantification of their structural characteristics, thereby significantly enhancing monitoring efficiency and convenience. By better assessing the layout of artificial reefs and their ecological impact, this approach offers valuable data support for the future planning and implementation of reef projects.