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.

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring.

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 study proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. In view of the problems faced by the current fishing port management department, such as low efficiency of fishing vessel size measurement methods and difficulty in updating the size information of large quantities of fishing vessels in time, this paper proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. (YOLO-LFVM). The model incorporates lightweight modules, such as MobileNetV3, AKConv, and C2f, and utilizes Python scripts in conjunction with OpenCV to measure vessel size in pixels. The findings indicate that, compared to the original model, the YOLO-LFVM model’s accuracy rate, recall rate, and mAP@0.5 decrease by only 0.7%, 0.2%, and 0.3%, respectively, while mAP@0.95 increases by 1.7%. Additionally, the model’s parameters decrease by 65%, and GFLOPs decrease by 69%. When comparing the model’s output with actual vessel data, the average relative error for total length is 2.67%, and for width, it is 3.28%. The research shows that the YOLO-LFVM model is effective in ship identification, ship tracking statistics, and measurement. Through the integration with UAV remote sensing technology, it is conducive to the timely updating of large-scale fishing vessel size information. Finally, the model can assist the daily management and law enforcement of the fishing port management department and can be applied to other equipment with limited computing power to perform target detection and object size measurement tasks.