arXiv:2608.23479v1 Announce Type: new
Abstract: Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping, yet automated perception and cross-modal alignment are severely bottlen...
By Taqi Hamoda, Nuno Gracias
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 presents a weakly supervised semantic segmentation approach for mapping seagrass habitats using side‑scan sonar imagery. By training a ViT‑based encoder‑decoder with image‑level labels, class activation maps are refined into pseudo‑labels and iteratively self‑trained, achieving high mean intersection‑over‑union scores (up to 89.3 %) without pixel‑level annotations. The method also benefits from self‑supervised pretraining and demonstrates generalizability in field trials.
By Hayat Rajani, Nuno Gracias, Rafael Garcia
arXiv:2608.24594v1 Announce Type: new
Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation...
By Sundarabalan Balasubramanian, C\'esar Borja, Ana C. Murillo, Lexi N. Wilkes, Meredith L. McPherson, Kira A. Krumhansl, Jennifer A. Dijkstra, Jarrett E. K. Byrnes
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
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