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Multispectral Object Detection On Flir 1

Metrics

mAP
mAP50

Results

Performance results of various models on this benchmark

Paper TitleRepository
MMPedestron-86.4%When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
RGB-X Scene Adaptive CBAM47.1%86.16%RGB-X Object Detection via Scene-Specific Fusion Modules
CAFF-DINO50.5%85.5%--
RSDet43.8%83.9%Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
CMX-82.2%CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
UniRGB-IR44.1%81.4%UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning
MiPa44.8%81.3%MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection
CSSA41.3%79.2%Multimodal Object Detection by Channel Switching and Spatial Attention-
CFT-77.7%Cross-Modality Fusion Transformer for Multispectral Object Detection
ProbEn37.9%75.5%Multimodal Object Detection by Channel Switching and Spatial Attention-
GAFF37.4%74.6%Multimodal Object Detection by Channel Switching and Spatial Attention-
YOLOv5 (T)-73.9%Cross-Modality Fusion Transformer for Multispectral Object Detection
GAFF (ResNet18)-72.9%Guided Attentive Feature Fusion for Multispectral Pedestrian Detection-
GAFF (VGG16)-72.7%Guided Attentive Feature Fusion for Multispectral Pedestrian Detection-
CFR_3 (VGG16)-72.4%Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
Halfway Fusion (VGG16)-71.2%Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
YOLOv5 (RGB)-67.8%Cross-Modality Fusion Transformer for Multispectral Object Detection
Halfway Fusion35.8%-Multimodal Object Detection by Channel Switching and Spatial Attention-
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Multispectral Object Detection On Flir 1 | SOTA | HyperAI