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SOTA
多光谱物体检测
Multispectral Object Detection On Flir 1
Multispectral Object Detection On Flir 1
评估指标
mAP
mAP50
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
mAP
mAP50
Paper Title
Repository
ProbEn
37.9%
75.5%
Multimodal Object Detection by Channel Switching and Spatial Attention
-
CFT
-
77.7%
Cross-Modality Fusion Transformer for Multispectral Object Detection
-
MMPedestron
-
86.4%
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
-
CAFF-DINO
50.5%
85.5%
-
-
UniRGB-IR
44.1%
81.4%
UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning
-
CSSA
41.3%
79.2%
Multimodal Object Detection by Channel Switching and Spatial Attention
-
YOLOv5 (T)
-
73.9%
Cross-Modality Fusion Transformer for Multispectral Object Detection
-
YOLOv5 (RGB)
-
67.8%
Cross-Modality Fusion Transformer for Multispectral Object Detection
-
GAFF (ResNet18)
-
72.9%
Guided Attentive Feature Fusion for Multispectral Pedestrian Detection
CMX
-
82.2%
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
-
MiPa
44.8%
81.3%
MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection
-
RGB-X Scene Adaptive CBAM
47.1%
86.16%
RGB-X Object Detection via Scene-Specific Fusion Modules
-
GAFF (VGG16)
-
72.7%
Guided Attentive Feature Fusion for Multispectral Pedestrian Detection
GAFF
37.4%
74.6%
Multimodal Object Detection by Channel Switching and Spatial Attention
-
RSDet
43.8%
83.9%
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
-
Halfway Fusion (VGG16)
-
71.2%
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
-
Halfway Fusion
35.8%
-
Multimodal Object Detection by Channel Switching and Spatial Attention
-
CFR_3 (VGG16)
-
72.4%
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
-
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