SonarLLM is a multimodal large language model that treats sonar as a native perceptual modality, combining a sonar‑specific encoder, physics‑aware feature enhancement, and reliability‑aware hierarchical fusion to align acoustic structure with optical semantics. The authors introduce SonarBench, a benchmark covering recognition, counting, visual question answering, and captioning across sonar‑only, optical‑only, and fusion settings, enabling controlled measurement of cross‑modal complementarity. SonarLLM achieves 72.0% macro accuracy on sonar‑only tasks and 68.7% under fusion, outperforming baselines by significant margins and demonstrating increasing fusion gains as optical visibility degrades.
By Cong Su, longxuan ma, Ling Dong, Guofeng Tang, Weijie Yin, Haohui Chen, Zhengtao Yu
The paper introduces SASC-USOD, a framework for underwater salient object detection that learns spatially adaptive coordination between two structural representations: a boundary-sensitive representation using Laplacian filtering and a region-coherent representation via dual-range anisotropic large-kernel aggregation. A spatial coordination module estimates the relative reliability of these representations and adaptively blends them based on image content. Experiments on USOD10K and USOD benchmarks show that SASC-USOD outperforms existing methods, reducing MAE by 4.07% and 23.53% respectively, and its lightweight variant achieves 21 FPS on an NVIDIA Jetson TX2 NX.
By Lin Hong, Chenhui Wang, Linan Deng, Yuning Cui, Yu Zhang, Xin Wang, Bojian Zhang, Xingchen Yang, Fumin Zhang
AquaBEV is a monocular underwater occupancy model that predicts local bird’s‑eye‑view (BEV) occupancy from a single RGB image. It uses paired 3D imaging sonar data as geometric supervision during training, mapping visual features into a calibration‑free polar representation and decoding along the range dimension before reconstructing Cartesian BEV coordinates. In a controlled underwater occupancy benchmark, AquaBEV outperforms the strongest transferred baseline with 31.4 % Visible IoU and 38.6 % Observed IoU, achieving 4.0 % and 4.3 % relative improvements respectively.
By Trung Tien Dong, Shengji Jin, Chen Chen, Yi Sheng, Xiaomin Lin
arXiv:2605.24928v2 Announce Type: replace
Abstract: Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel cover...
By Hui Lin, Jiayi Li, Jing Wang, Shenghui Rong
The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.
By Dan Zimmerman, Frank E. Bobe III, Amelia L. McCormack, Matthew Cook, Gregory D. Vetaw
arXiv:2608.23215v1 Announce Type: cross
Abstract: Automated perception in side-scan sonar (SSS) imagery is severely hindered by physical acoustic artifacts, resulting in representations that inextric...
By Taqi Hamoda, Hayat Rajani, Nuno Gracias
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
RSFusionDet introduces a new RGB‑Sonar multimodal object detection dataset (RSFusion) and benchmark metrics for underwater imaging. The proposed detector uses a Cross‑Attention Fusion module to align RGB and sonar features and an Object Matching Head with loss to identify identical objects across modalities. On the RSFusion dataset, RSFusionDet achieves 76.4/48.6 AP for RGB/sonar detection and 83.4 F1‑Score for cross‑modal matching, outperforming existing models and improving over the DINO baseline by 0.7/1.4 AP.
By Zhuoyan Liu, Yihan Wang, Bo Wang, Bing Wang, Ye Li
Reconstructing photorealistic scenes in unconstrained underwater environments remains challenging due to severe media-induced light scattering and unpredictable dynamic objects. Recent feed-forward vi...
WaterClear-GS introduces a physics-informed Gaussian splatting method tailored for underwater 3D reconstruction and appearance restoration. It models underwater degradation as intrinsic Gaussian attributes and employs a dual-branch optimization that separates clean appearance from degradation while preserving photometric consistency. The approach incorporates depth-guided geometry regularization, perception-driven supervision, exposure constraints, adaptive regularization, and spectral regularization, achieving strong novel view synthesis and image restoration performance at over 160 FPS.
By Xinrui Zhang, Yufeng Wang, Zesheng Wang, Dacheng Qi, Wenrui Ding, Shuangkang Fang
The paper presents a pipeline for constructing accurate ground‑truth annotations for visual tracking across the air–water interface, synchronizing camera frames with motion‑capture data and correcting underwater projections. It introduces a Cross‑Medium Recovery Policy (CMRP) that uses confidence‑triggered template selection to improve MixFormerV2’s tracking performance without retraining the backbone. CMRP raises Macro Success AUC by 2.95 points and increases tracking coverage while reducing recovery latency.
By Jiangong Xiao, Zhe Sun, Kanzhong Yao, Yuanbo Bi, Haofei Zhao, Ruixuan Hu, Guan Huang, Xuelong Li
uScenes is a new multimodal dataset for underwater robot perception that provides synchronized 3D multibeam sonar point clouds and RGB imagery. It comprises 110 scenes with 95,834 observations, totaling 277.6 minutes of data collected during multiple field sessions. The dataset aims to support research in underwater sensor fusion, cross‑modal representation learning, and 3D scene understanding.
By Trung Tien Dong, Zhenqi Wu, Aditya Penumarti, Zi-Hao Zhang, Micaiah Bartlett, Jane Shin, Xiaomin Lin