Haitao Gu

Joint segmentation of heterogeneous side-scan sonar datasets requires learning from different acoustic distributions and annotation scopes. Dataset-aware masked cross-entropy attenuates classes outside each dataset’s annotation scope, potentially reducing incorrect negative supervision while weakening cross-class competition. This study evaluates this trade-off using AI4Shipwrecks, MCM, and SubPipe in a shared four-class space. Experiments compare unmasked cross-entropy, soft attenuation, and hard exclusion across input representations and sampling policies. With five-channel SegFormer-B2, soft DMCE achieved a mIoU of 0.4908, below unmasked cross-entropy at 0.5234 in all five matched seeds; their foreground macro-IoU values were 0.0409 ± 0.0360 and 0.0694 ± 0.0413, respectively. After averaging the two repeated runs within each seed, hard masking achieved a descriptive macro mIoU of 0.1544 ± 0.0670 and a foreground macro IoU of 0.0144 ± 0.0153 across five seed-level summaries. Under PhysD input, the mean ordering reversed: soft DMCE achieved 0.5204 versus 0.4952 for cross-entropy. Dataset-balanced sampling increased cross-entropy performance to a macro mIoU of 0.5505 and a foreground macro IoU of 0.1138 ± 0.0144. Pipeline IoU nevertheless remained near zero despite SubPipe mIoU approaching 0.5, and held-out foreground IoU was zero when the only positive supervision for a category was excluded. Overall, the effect of attenuation varied across training configurations. These results show that background-inclusive macro mIoU should be interpreted alongside foreground-specific metrics when assessing joint segmentation performance.

The detection and localization of large underwater targets are important for maritime security, marine resource exploration, and underwater situational awareness, while the increasing acoustic stealth of underwater vehicles has limited conventional acoustic methods. This review provides a systematic overview of non-acoustic detection and localization technologies for large underwater targets, with emphasis on their relevance to unmanned aerial, surface, and underwater platforms. Wake-based detection, magnetic anomaly detection (MAD), and gravity anomaly detection (GAD) are reviewed as three representative non-acoustic routes. A bibliometric analysis is first conducted to summarize research trends, major contributors, and emerging hotspots. Wake-based methods are discussed in terms of wake signatures, modeling approaches, sensing platforms, and localization potential. MAD is analyzed from the perspectives of magnetic dipole modeling, target-based detection, noise-based detection, artificial intelligence (AI)-based detection, and magnetic localization. GAD is discussed with respect to physical feasibility, gravity-gradient target modeling, inversion methods, and engineering constraints. The review shows that wake-based methods are suitable for wide-area search and trajectory inference, MAD is relatively mature for short-range confirmation and localization, and GAD remains promising but less mature. Future research should focus on onboard sensors, platform stability, weak-signal extraction, background suppression, quantitative evaluation metrics, multi-source fusion, autonomous mission planning, and multi-platform collaboration.

Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To address these issues, this study proposes an improved YOLOv11 network named YOLOv11-SDC. Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model’s receptive field. A Content-Guided Attention Fusion (CGAF) module is also incorporated prior to the detection module to assign appropriate weights to various feature maps, thereby emphasizing the relevant object information. Experimental results clearly demonstrate the superiority of YOLOv11-SDC over several iterations of YOLO versions in detection performance. The proposed method was validated through extensive real-world experiments, yielding a precision of 0.934, recall of 0.698, mAP@0.5 of 0.825, and mAP@0.5:0.95 of 0.598. In conclusion, the improved YOLOv11-SDC offers a promising solution for detecting small objects in SSS images, showing substantial potential for marine applications.

This study verifies the effects of deployment parameters on the safe separation of Autonomous Underwater Vehicles (AUVs) and mission payloads. The initial separation phase is meticulously modeled based on computational fluid dynamics (CFD) simulations employing the cubic constitutive Shear Stress Transport (SST) k-ω turbulence model and overset grid technologies. This phase is characterized by a 6-degree-of-freedom (6-DOF) framework incorporating Dynamic Fluid-Body Interaction (DFBI), supported by empirical validation. The SST k-ω turbulence model demonstrates superior performance in managing flows characterized by adverse pressure gradients and separation. DFBI entails computationally modeling fluid–solid interactions during motion or deformation. The utilization of overset grids presents several advantages, including enhanced computational efficiency by concentrating computational resources solely on regions of interest, simplified handling of intricate geometries and moving bodies, and adaptability in adjusting grids to accommodate changing simulation conditions. This research analyzes mission payloads’ trajectories and attitude adjustments after release from AUVs under various cruising speeds and initial release dynamics, such as descent and angular velocities. Additionally, this study evaluates the effects of varying ocean currents at different depths on separation safety. Results indicate that the interaction between AUVs and mission payloads during separation increases under higher navigational speeds, reducing the separation speed and degrading the stability. As the initial drop velocities increase, fast transition through the AUV’s immediate flow field promotes separation. The core of this process is the initial pitch angle management upon deployment. Optimizing initial pitching angular velocity prolongs the time for mission payloads to reach their maximum pitch angle, thus decreasing horizontal displacement and improving separation safety. Deploying AUVs at greater depths alleviates the influence of ocean currents, thereby reducing disturbances during payload separation.