SOTAVerified

Multispectral Object Detection

Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has be shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts.

Papers

Showing 110 of 39 papers

TitleStatusHype
YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection FrameworkCode4
UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter TuningCode2
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object DetectionCode2
CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection under Adverse Weather ConditionsCode2
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object DetectionCode2
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with TransformersCode2
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark DatasetCode2
INSANet: INtra-INter Spectral Attention Network for Effective Feature Fusion of Multispectral Pedestrian DetectionCode1
Fully Convolutional Networks for Semantic SegmentationCode1
Cross-Modality Fusion Transformer for Multispectral Object DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMPedestronmAP5086.4Unverified
2RGB-X Scene Adaptive CBAMmAP5086.16Unverified
3CAFF-DINOmAP5085.5Unverified
4RSDetmAP5083.9Unverified
5CMXmAP5082.2Unverified
6UniRGB-IRmAP5081.4Unverified
7MiPamAP5081.3Unverified
8CSSAmAP5079.2Unverified
9CFTmAP5077.7Unverified
10ProbEnmAP5075.5Unverified
#ModelMetricClaimedVerifiedStatus
1FusionRPN+BFAll Miss Rate51.7Unverified
2Halfway FusionAll Miss Rate49.18Unverified
3IATDNN+IASSAll Miss Rate48.96Unverified
4IAFR-CNNAll Miss Rate44.23Unverified
5CIANAll Miss Rate35.53Unverified
6AR-CNNAll Miss Rate34.95Unverified
7MSDS-R-CNNAll Miss Rate34.15Unverified
8MBNetAll Miss Rate31.87Unverified
9TSFADetAll Miss Rate30.74Unverified
10CMPDAll Miss Rate28.98Unverified
#ModelMetricClaimedVerifiedStatus
1YOLOv3-4‐channelmAP@0.5:0.9564.4Unverified
2YOLOv3-EnsemblemAP@0.5:0.9553.4Unverified
#ModelMetricClaimedVerifiedStatus
1CFTmAP5097.5Unverified