Evaluation Method for Artificial Reefs Based on Multi-Object Tracking

Artificial reefs (ARS) are structures placed in the ocean, and their posture and position affect the placement effect of the reef. Quantitative data on the reef posture and position can provide references for the creation of a favorable environment for marine organisms. This paper focuses on improving the quality assessment methods for the deployment of ARS in marine habitats. Based on the images of ARS detected by forward-looking sonar, a new method is proposed to determine the three-dimensional position and deployment effectiveness of the reefs, with the aim of obtaining more detailed information about the ARS to assist in evaluating the quality of their deployment. By constructing a multi-object tracking (MOT) dataset based on the Oculus sonar, the YOLOv8n-pose series model was used for object detection. Subsequently, the three-dimensional position and subsidence of ARS were evaluated using trajectories identified by the MOT algorithm. This paper compares various MOT algorithms and improves the BOTSORT algorithm by introducing the Hungarian algorithm and the Joseph form of the Kalman filter, significantly enhancing the accuracy of target tracking. The experimental results demonstrate that, within the framework of the YOLOv8n-pose model, the detection and association accuracy of the BOTSORT algorithm, which has been refined to address the characteristics of sonar images, has been further enhanced. Additionally, this paper proposes a mathematical modeling method for the assessment of ARS and their subsidence, providing important technical support and solutions for future marine ranch construction.