Papers Robust Object Detection
“Robust Object Detection” 태그가 달린 논문 113편 · 필터 해제
A Synthetic Dataset for Manometry Recognition in Robotic Applications
This paper addresses the challenges of data scarcity and high acquisition costs in training robust object detection models for complex industrial environments, such as offshore oil platforms. Data collection in these haz…
Synthetic Data GenerationRobust Object DetectionVideo GenerationBeyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environments (e.g., nighttime driving, tunnels), hi…
Robust Object DetectionOD-VIRAT: A Large-Scale Benchmark for Object Detection in Realistic Surveillance Environments
Realistic human surveillance datasets are crucial for training and evaluating computer vision models under real-world conditions, facilitating the development of robust algorithms for human and human-interacting object d…
Robust Object DetectionBoosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability
Detectors often suffer from performance drop due to domain gap between training and testing data. Recent methods explore diffusion models applied to domain generalization (DG) and adaptation (DA) tasks, but still struggl…
Domain GeneralizationRobust Object DetectionSAM2Auto: Auto Annotation Using FLASH
Vision-Language Models (VLMs) lag behind Large Language Models due to the scarcity of annotated datasets, as creating paired visual-textual annotations is labor-intensive and expensive. To address this bottleneck, we int…
Instance SegmentationObjectobject-detectionObject Detection+5FMG-Det: Foundation Model Guided Robust Object Detection
Collecting high quality data for object detection tasks is challenging due to the inherent subjectivity in labeling the boundaries of an object. This makes it difficult to not only collect consistent annotations across a…
Multiple Instance LearningObjectobject-detectionObject Detection+1VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sensor inputs. As a result, they struggle t…
Autonomous DrivingLanguage ModelingLanguage Modellingobject-detection+3LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
We present an embodied robotic system with an LLM-driven agent-orchestration architecture for autonomous household object management. The system integrates memory-augmented task planning, enabling robots to execute high-…
In-Context LearningObjectobject-detectionObject Detection+5Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors
Detecting small objects remains a significant challenge in single-shot object detectors due to the inherent trade-off between spatial resolution and semantic richness in convolutional feature maps. To address this issue,…
DescriptiveObjectobject-detectionObject Detection+2High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving
Object detection precision is crucial for ensuring the safety and efficacy of autonomous driving systems. The quality of acquired images directly influences the ability of autonomous driving systems to correctly recogniz…
Autonomous DrivingAutonomous VehiclesImage Reconstructionobject-detection+2WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer
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 ed…
Objectobject-detectionObject DetectionRobust Object DetectionRobust Object Detection of Underwater Robot based on Domain Generalization
Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and location. In recent years, researchers tend to apply object detection to un…
Domain GeneralizationObjectobject-detectionObject Detection+1Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection
Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models h…
Domain AdaptationDomain Generalizationobject-detectionObject Detection+3RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection
While recent low-cost radar-camera approaches have shown promising results in multi-modal 3D object detection, both sensors face challenges from environmental and intrinsic disturbances. Poor lighting or adverse weather …
3D Object DetectionObjectobject-detectionObject Detection+1UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection
Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to severe challenges, namely, their limited computation, energy and communication…
Objectobject-detectionObject DetectionRobust Object Detection+2Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention
Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for robust object detection. While Spiking Neural Networks (SNNs) on neuromorphic hardware are often con…
object-detectionObject DetectionRobust Object DetectionEvaluating the Adversarial Robustness of Detection Transformers
Robust object detection is critical for autonomous driving and mobile robotics, where accurate detection of vehicles, pedestrians, and obstacles is essential for ensuring safety. Despite the advancements in object detect…
Adversarial RobustnessAutonomous DrivingObjectobject-detection+2PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection
Single-Domain Generalized Object Detection~(S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector. Existing S-DGOD appr…
Data AugmentationDomain GeneralizationObject DetectionRobust Object DetectionImproving Batch Normalization with TTA for Robust Object Detection in Self-Driving
In current open real-world autonomous driving scenarios, challenges such as sensor failure and extreme weather conditions hinder the generalization of most autonomous driving perception models to these unseen domain due …
Autonomous Drivingobject-detectionObject DetectionRobust Object Detection+1Multimodal Object Detection using Depth and Image Data for Manufacturing Parts
Manufacturing requires reliable object detection methods for precise picking and handling of diverse types of manufacturing parts and components. Traditional object detection methods utilize either only 2D images from ca…
Objectobject-detectionObject DetectionRobust Object Detection