SEAF-Net: A Sustainable and Lightweight Attention-Enhanced Detection Network for Underwater Fish Species Recognition - Yu-Shan Han; Sheng-Lun Zhao; Chu Chen; Kangning Cui; Pingfan Hu; Rui-Feng Wang (Journal of Marine Science and Engineering, 2026)
This study presents SEAF-Net, a lightweight and efficient detection network designed for low-contrast and highly dynamic underwater environments. Built upon YOLOv11n, SEAF-Net introduces three complementary structural enhancements: (1) Omni-Dimensional Dynamic Convolution (ODConv) to improve adaptive modeling of multi-scale and directional texture variations; (2) SimA-SPPF, which embeds the SimAM attention mechanism into the SPPF module to enable neuron-level saliency reweighting and effective suppression of complex background interference; and (3) GhostC3k2 to reduce redundant computation while preserving sufficient representational capacity. Evaluated on a standardized 13-class underwater fish dataset under a unified training and evaluation protocol, SEAF-Net achieves 6.1 GFLOPs, 92.683% Precision, 88.459% Recall, 93.333% mAP50, 73.445% mAP, and a 90.522% F1-score. Compared with the YOLOv11n baseline, SEAF-Net improves F1-score and Recall by 0.510% and 0.575%, respectively, while reducing computational cost by approximately 6%, demonstrating a favorable accuracy–efficiency trade-off under lightweight constraints. Ablation results further confirm that SimA-SPPF plays a dominant role in background suppression, ODConv consistently enhances deformation and directional texture modeling, and GhostC3k2 effectively controls computational overhead without degrading detection accuracy. To assess deployment feasibility, additional test set evaluations were conducted under deployment-oriented conditions using resource-limited hardware, yielding an F1-score of 88.54%. This result confirms that the proposed model maintains stable detection performance and robustness beyond training and validation stages. Overall, SEAF-Net provides an effective balance of accuracy, efficiency, and robustness, offering practical support for low-carbon, scalable, and sustainable intelligent aquaculture monitoring and underwater ecological assessment in real-world environments.

Noise Characteristics and Shallow Subsurface Structure Detection in Coastal Zones: A Case Study from Dong’ao Island, Zhuhai - Siqing Liu; Sixu Han; Yongzhi Liang; Shuji Yang; Yi Chai; Tongying Hu; Ruifeng Wu; Yu Li; Qingxian Zhao; Zengjia Li; Wei Zhang; Xianqing Wang; et al. (Journal of Marine Science and Engineering, 2026)
Shallow subsurface structure detection in coastal zones serves as a critical foundation for resource development and engineering construction. However, conventional geophysical methods exhibit significant limitations in land–sea transition zones, where pronounced “boundary effects” create substantial “exploration gaps” due to difficulties in merging terrestrial and marine datasets. To achieve truly seamless subsurface imaging across the coastal boundary, this study develops and implements an integrated cross-boundary survey approach utilizing nodal seismometers and seismic ambient noise. At Dong’ao Island, Zhuhai, we deployed a comprehensive seismic profile spanning hillside, sandbeach, and seafloor environments to evaluate the method’s applicability in complex coastal settings systematically. Results demonstrate substantially stronger ambient noise energy in submarine environments compared to terrestrial settings. All stations recorded abundant and stable high-frequency (>1 Hz) noise signals, which are adequate for shallow subsurface imaging. Rayleigh wave dispersion curves extracted via the advanced Frequency-Bessel transform method enabled inversion of a continuous 2D shear-wave velocity profile along the survey line. Bedrock interface depths determined using the Horizontal-to-Vertical Spectral Ratio (HVSR) method showed remarkable consistency with the bedrock morphology revealed by the shear-wave velocity structure, validating the reliability of our approach in coastal environments. This research successfully demonstrates the feasibility of seismic ambient noise imaging as a bridging technique for land–sea exploration, providing an efficient, environmentally friendly, and continuous technical solution to overcome coastal zone exploration challenges.

A Hybrid CUBE-IForest Approach for Outlier Detection in Multibeam Bathymetry - Rui Han; Yukai Hong; Xibin Han; Yi Zhang; Shunming Hu; Yuan Huan; Xiaodong Cui; Xiaohu Li (Journal of Marine Science and Engineering, 2026)
With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis.

Underwater Target Detection with High Accuracy and Speed Based on YOLOv10 - Zhengliang Hu; Le Cheng; Shui Yu; Pan Xu; Peng Zhang; Rui Tian; Jingqi Han (Journal of Marine Science and Engineering, 2025)
Underwater target detection exhibits extensive applications in marine target exploration and marine environmental monitoring. However, conventional images of underwater targets present challenges including blurred contour information, complex environmental conditions, and pronounced scattering effects. In this work, an underwater target detection method based on YOLOv10 is designed, and the detection performance is compared with the YOLOv5 model. Experimental results demonstrate that the YOLOv10 model has a mAP50 of 85.6% on the URPC 2020 dataset, improving the mAP50 by 1.2% than that of YOLOv5. This model exhibits high detection accuracy and high proceeding speed, which provides a promising support for precise and fast underwater target detection.