WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Graz University of Technology（格拉茨工业大学）；Virtual Vehicle Research（虚拟车辆研究院）
Huilin Yin, Pengyu Wang, Senmao Li, Jun Yan, Daniel Watzenig
Robust object detection for Unmanned Surface Vehicles (USVs) in complex water environments is essential for reliable navigation and operation. Specifically, water surface object detection faces challenges from blurred edges and diverse object scales. Although vision-radar fusion offers a feasible solution, existing approaches suffer from cross-modal feature conflicts, which negatively affect model robustness. To address this problem, we propose a robust vision-radar fusion model WS-DETR. In particular, we first introduce a Multi-Scale Edge Information Integration (MSEII) module to enhance edge perception and a Hierarchical Feature Aggregator (HiFA) to boost multi-scale object detection in the encoder. Then, we adopt self-moving point representations for continuous convolution and residual connection to efficiently extract irregular features under the scenarios of irregular point cloud data. To further mitigate cross-modal conflicts, an Adaptive Feature Interactive Fusion (AFIF) module is introduced to integrate visual and radar features through geometric alignment and semantic fusion. Extensive experiments on the WaterScenes dataset demonstrate that WS-DETR achieves state-of-the-art (SOTA) performance, maintaining its superiority even under adverse weather and lighting conditions.
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces
University of Liverpool（利物浦大学）；Wuhan University of Technology（武汉理工大学）；Nantong University（南通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
physfusion: A Transformer-based Dual-Stream Radar and Vision Fusion Framework for Open Water Surface Object Detection
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
RETR: Multi-View Radar Detection Transformer for Indoor Perception
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））；University of Bologna（博洛尼亚大学）；Information Technology R&D Center (ITC), Mitsubishi Electric Corporation（三菱电机公司信息技术研发中心（ITC））
Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；JITRI（江苏省产业技术研究院）；Nantong University（南通大学）
calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能与智能交通研究部）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进科技学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation
Division of Water and Environmental Engineering, KTH Royal Institute of Technology（水与环境工程系，皇家理工学院）；Division of Software and Computer Systems, KTH Royal Institute of Technology（软件与计算机系统系，皇家理工学院）；Department of Physical Geography, Stockholm University（物理地理系，斯德哥尔摩大学）
WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radar
Institute of Deep Perception Tech, JITRI（JITRI深度感知技术研究所）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；Faculty of Science and Engineering, University of Liverpool（利物浦大学科学与工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, HKUST (Guangzhou)（香港科技大学（广州）人工智能学科及智能交通学科）
National Scientific and Technical Research Council (CONICET)（国家科学技术研究委员会（CONICET））；Institute of Human Development, National University of General Sarmiento（萨米恩托将军国立大学人类发展研究所）；Faculty of Natural Sciences, National University of La Pampa（拉潘帕国立大学自然科学学院）
A method for segmenting water bodies in optical and synthetic aperture radar (SAR) satellite images is proposed. It makes use of the textural features of the different regions in the image for segmentation. The method consists in a multiscale analysis of the images, which allows us to study the images regularity both, locally and globally. As results of the analysis, coarse multifractal spectra of studied images and a group of images that associates each position (pixel) with its corresponding value of local regularity (or singularity) spectrum are obtained. Thresholds are then applied to the …
Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, USA（农业与生物工程系，密苏里州立大学）；Sustainable Agriculture Research Unit, Mississippi State, MS, USA（可持续农业研究单位，密苏里州）；Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, USA（植物与土壤科学系，密苏里州立大学）；Biosystems Engineering, North Dakota State University, Fargo, ND, USA（生物系统工程系，北达科塔州立大学）；NDSU Peltier Institute for the Advancement of Agricultural Technology, North Dakota State University, Fargo, ND, USA（NDSU裴特里农业技术进步研究所，北达科塔州立大学）
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
