arXiv Computer Vision
MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection
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Robust Cross-Modal Foundation Model Perception for Underwater Robots under Degraded Visual Conditions
arXiv:2608. 19710v1 Announce Type: cross Abstract: Reliable underwater robotic perception remains difficult because optical imagery degrades under turbidity, wavelength-dependent attenuation, low illumination, scattering, and blur.
arXiv Computer Vision
Aug 25
Spiking Neural Networks for Energy-Efficient Object Detection in Forward-Looking Sonar Imagery
arXiv:2608.22072v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-const...
arXiv AI
Sep 21
LoRA Enhanced Contrastive Learning with SAS Vision Transformers
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.
Hugging Face Trending Papers
Jul 27
LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection
Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference.
arXiv Computer Vision
Sep 22
HDMamba-YOLO: Efficient State-Space Perception and Local Spatial Reconstruction for UAV Small Object
arXiv:2609.23061v1 Announce Type: new Abstract: Small-object detection in UAV imagery is challenged by weak visual evidence, ambiguous boundaries, dense object distributions, and complex backgrounds....
arXiv Computer Vision
Aug 27
RSFusionDet: Underwater RGB-Sonar Multimodal Object Detection
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.