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.

To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection.

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.

This article presents a summary of three common false-bottom occurrences in echosounder imaging based on an analysis of echosounder data. Utilizing the imaging principle of the echosounder, a comprehensive analysis was conducted and an explanation of each situation’s causes, imaging characteristics, impacts, and solutions is presented. Additionally, the article includes calculations to determine the precise location of the false bottom, which were subsequently validated through actual data collection. To address the two most impactful false-bottom scenarios in target detection, solutions are proposed from two perspectives. By accurately judging the position and imaging characteristics of these false bottoms, the article concludes with an analysis of the causes of false bottoms and presents corresponding solutions. The article aims to facilitate the rapid identification and elimination of false bottoms, thus mitigating their adverse effects on target detection.