Papers Radar Object Detection
“Radar Object Detection” 태그가 달린 논문 29편 · 필터 해제
RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning
Semantic 3D city models are worldwide easy-accessible, providing accurate, object-oriented, and semantic-rich 3D priors. To date, their potential to mitigate the noise impact on radar object detection remains under-explo…
Objectobject-detectionObject DetectionRadar Object Detection+1SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection
Recently, 4D Radar has emerged as a crucial sensor for 3D object detection in autonomous vehicles, offering both stable perception in adverse weather and high-density point clouds for object shape recognition. However, p…
3D Object DetectionAutonomous DrivingAutonomous VehiclesObject+3TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection
Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which…
Autonomous Drivingobject-detectionObject DetectionRadar Object DetectionMask-RadarNet: Enhancing Transformer With Spatial-Temporal Semantic Context for Radar Object Detection in Autonomous Driving
As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years, making it an appealing complement to commonly used sensors like camera and LiDAR in autonomous driving. Radio…
Autonomous Drivingobject-detectionObject DetectionRadar Object DetectionSCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection
3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds simila…
3D Object DetectionAutonomous VehiclesKnowledge Distillationobject-detection+3DSFEC: Efficient and Deployable Deep Radar Object Detection
Deploying radar object detection models on resource-constrained edge devices like the Raspberry Pi poses significant challenges due to the large size of the model and the limited computational power and the memory of the…
Objectobject-detectionObject DetectionRadar Object DetectionMUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection
In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to LiDAR. However, the sparsity of radar poi…
Autonomous DrivingObjectobject-detectionObject Detection+1RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However, obtaining annotated datasets from real radar images, crucial for training these networks, is challen…
Autonomous Drivingobject-detectionObject DetectionRadar Object DetectionCRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation
In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still, LiDAR is relatively high cost, which hinders adoption of this technology for consume…
3D Object DetectionAutonomous DrivingKnowledge Distillationobject-detection+2Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection
In recent years, driven by the need for safer and more autonomous transport systems, the automotive industry has shifted toward integrating a growing number of Advanced Driver Assistance Systems (ADAS). Among the array o…
Contrastive LearningObjectobject-detectionObject Detection+3MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection
4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions, thus playing a pivotal role in autonomous driving. While cameras and LiDAR are typically the primary sensors used in perc…
3D Object DetectionAutonomous DrivingAutonomous Vehiclesobject-detection+2RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding
Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted wit…
3D Object DetectionObjectobject-detectionObject Detection+1Exploiting Sparsity in Automotive Radar Object Detection Networks
Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based …
Autonomous DrivingObjectobject-detectionObject Detection+1Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. However, the transfer from irregular point cl…
Descriptiveobject-detectionObject DetectionRadar Object DetectionRadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model
The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the increasing use of LiDAR sensors and poin…
Autonomous DrivingDeep Learningobject-detectionObject Detection+1T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals
Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such…
Objectobject-detectionObject DetectionRadar Object DetectionA recurrent CNN for online object detection on raw radar frames
Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light cond…
Objectobject-detectionObject DetectionRadar Object DetectionDAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps
Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and…
2D Object DetectionObjectobject-detectionObject Detection+1Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms of generating its own training signal fo…
object-detectionObject DetectionRadar Object DetectionK-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions
Unlike RGB cameras that use visible light bands (384$\sim$769 THz) and Lidars that use infrared bands (361$\sim$331 THz), Radars use relatively longer wavelength radio bands (77$\sim$81 GHz), resulting in robust measurem…
3D Object DetectionAutonomous DrivingObjectobject-detection+2