Papers Multispectral Object Detection
“Multispectral Object Detection” 태그가 달린 논문 52편 · 필터 해제
CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection
Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditions. However, differences in modality appearance and reliability can intro…
Multispectral Object DetectionComputational EfficiencyFully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection
Existing multispectral detectors are limited by discrete spectral processing, a scale-dependent shift in the relative reliability of spectral and spatial cues across pyramid levels, and the lack of explicit rotation-equi…
Multispectral Object DetectionDual Sparse Aggregation Transformer for Multispectral Object Detection
Transformer-based approaches have obtained excellent performance in multispectral object detection tasks due to their ability to model long-range dependencies and capture complementary information. However, previous tran…
Multispectral Object DetectionProgressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection
Effective cross-modal feature alignment and interaction are central challenges in multispectral object detection. Although global cross-attention provides strong long-range modeling ability, its quadratic complexity with…
Multispectral Object DetectionSemantic correspondenceLong-range modelingSpectraDINO: Modality-Conditioned Adaptation of RGB Vision Foundation Models Across Infrared Bands
Vision foundation models (VFMs) pretrained on large-scale RGB data provide strong general-purpose representations, yet infrared perception, which is essential for robotics and driving in low light and adverse weather, st…
Multispectral Object DetectionSemantic SegmentationBridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection
Text-guided multispectral object detection uses text semantics to guide semantic-aware cross-modal interaction between RGB and IR for more robust perception. However, notable limitations remain: (1) existing methods ofte…
Multispectral Object DetectionFrom Words to Wavelengths: VLMs for Few-Shot Multispectral Object Detection
Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception under diverse illumination conditions is essential. However, the limited a…
Multispectral Object DetectionAutonomous DrivingMODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images
Aerial object detection faces significant challenges in real-world scenarios, such as small objects and extensive background interference, which limit the performance of RGB-based detectors with insufficient discriminati…
Multispectral Object DetectionSFFR: Spatial-Frequency Feature Reconstruction for Multispectral Aerial Object Detection
Recent multispectral object detection methods have primarily focused on spatial-domain feature fusion based on CNNs or Transformers, while the potential of frequency-domain feature remains underexplored. In this work, we…
Multispectral Object DetectionFSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data
Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we explore this complex task and introduce a …
Multispectral Object DetectionIRDFusion: Iterative Relation-Map Difference guided Feature Fusion for Multispectral Object Detection
Current multispectral object detection methods often retain extraneous background or noise during feature fusion, limiting perceptual performance. To address this, we propose an innovative feature fusion framework based …
Multispectral Object DetectionDEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
Multispectral object detection is an important application for unmanned aerial vehicles (UAVs). However, it faces several challenges. First, low-light RGB images weaken the multispectral fusion due to details loss. Secon…
Multispectral Object DetectionMultispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection
Modern multispectral feature fusion for object detection faces two critical limitations: (1) Excessive preference for local complementary features over cross-modal shared semantics adversely affects generalization perfor…
Multispectral Object DetectionSalient Object DetectionSemantic SegmentationYOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework
Multispectral object detection, which integrates information from multiple bands, can enhance detection accuracy and environmental adaptability, holding great application potential across various fields. Although existin…
Multispectral Object Detectionobject-detectionObject DetectionMultispectral Detection Transformer with Infrared-Centric Sensor Fusion
Multispectral object detection aims to leverage complementary information from visible (RGB) and infrared (IR) modalities to enable robust performance under diverse environmental conditions. In this letter, we propose IC…
Multispectral Object DetectionObjectobject-detectionObject Detection+2Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust f…
Multispectral Object DetectionObjectobject-detectionObject DetectionCAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion
Object detection on images can find benefit from coupling multiple spectra, each presenting specific useful features. However, building an efficient architecture coupling the different modalities is a complex task. Trans…
Multispectral Object DetectionObjectobject-detectionObject Detection+1Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges
Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of the…
Autonomous VehiclesMultispectral Object Detectionobject-detectionObject Detection+2When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
Recent years have witnessed increasing research attention towards pedestrian detection by taking the advantages of different sensor modalities (e.g. RGB, IR, Depth, LiDAR and Event). However, designing a unified generali…
3D Object DetectionMultispectral Object DetectionObject DetectionPedestrian DetectionRGB-T Object Detection via Group Shuffled Multi-receptive Attention and Multi-modal Supervision
Multispectral object detection, utilizing both visible (RGB) and thermal infrared (T) modals, has garnered significant attention for its robust performance across diverse weather and lighting conditions. However, effecti…
Multispectral Object DetectionObjectobject-detectionObject Detection