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Are We Ready for Unmanned Surface Vehicles in Inland Waterways? The USVInland Multisensor Dataset and Benchmark

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  • Basic Concept
  • Sensors
    • Camera
    • LiDAR
    • Radar
      • 调皮连续波：毫米波雷达传感器【专栏文章目录】
      • 毫米波雷达传感器基础知识
      • 2019 年中国毫米波雷达行业概览
    • 4D Radar
      • 汽车智能化系列之4D成像雷达：于锦上更添花，由侧幕登前台
      • Oculii Installation
      • Oculii Data
  • Sensor Fusion
    • Performance on NuScenes
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    • Background
    • Calibration
      • Coordinates
    • Radar Object Detection
    • Dataset
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      • Radar-Camera Fusion
      • Radar
      • Radar Architecture
    • 语料库
    • Experiments
  • USV
    • Architecture
    • Survey
    • Approaches
    • Background
    • Dataset
  • Papers
    • PointPillars
    • Fusion
      • Distant vehicle detection using radar and vision
      • RRPN: Radar Region Proposal Network for Object Detection in Automomous Vehicles
      • A Deep learning-based radar and camera sensor fusion architecture for object detection
      • Developing an On-Road Object Detection System Using Monovision and Radar Fusion
      • Radar Camera Fusion via Representation Learning in Autonomous Driving
      • Spatial Attention Fusion for Obstacle Detection UsingMmWave Radar and Vision Sensor
      • YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
      • Object Detection Using Multi-Sensor Fusion Based on Deep Learning
      • Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors
    • USV
      • Robust Small Object Detection on the Water Surface through Fusion of Camera and Millimeter Wave Radar
      • FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
      • Are We Ready for Unmanned Surface Vehicles in Inland Waterways? The USVInland Multisensor Dataset and Benchmark
    • Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
    • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
    • Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
    • RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-Based Obstacle Detection in Challenging Environments
    • RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
    • Automotive radar and camera fusion using Generative Adversarial Networks
    • Moving Target Classification in Automotive Radar System Using C-RNN
    • Automotive Radar Gridmap Representations
    • Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
    • CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
    • Experiments with mmWave Automotive Radar Test-bed
    • Practical classification of different moving targets using automotive radar and deep neural networks
    • Two-Stage Pedestrian Classification in Automotive Radar Systems
    • RODNet: Radar Object Detection Using Cross-Modal Supervision
    • Radar Based Object Detection and Tracking for Autonomous Driving
    • Coastal SLAM With Marine Radar for USV Operation in GPS-Restricted Situations
    • Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning
    • Probabilistic Oriented Object Detection in Automotive Radar
    • PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
    • Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
  • Datasets
    • Summary
  • Groups
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  • Methodology
    • Signal Processing Workflow and Radar Imaging
    • 优化方案
  • Courses
    • 深蓝-多传感器融合
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    • 齐次坐标
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    • Fast Fourier Transform
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