HyperAI

Multispectral Object Detection On Flir 1

Metriken

mAP
mAP50

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnamemAPmAP50
multimodal-object-detection-by-channel37.9%75.5%
cross-modality-fusion-transformer-for-77.7%
when-pedestrian-detection-meets-multi-modal-86.4%
Modell 450.5%85.5%
unirgb-ir-a-unified-framework-for-visible44.1%81.4%
multimodal-object-detection-by-channel41.3%79.2%
cross-modality-fusion-transformer-for-73.9%
cross-modality-fusion-transformer-for-67.8%
guided-attentive-feature-fusion-for-72.9%
cmx-cross-modal-fusion-for-rgb-x-semantic-82.2%
mipa-mixed-patch-infrared-visible-modality44.8%81.3%
rgb-x-object-detection-via-scene-specific47.1%86.16%
guided-attentive-feature-fusion-for-72.7%
multimodal-object-detection-by-channel37.4%74.6%
removal-and-selection-improving-rgb-infrared43.8%83.9%
multispectral-fusion-for-object-detection-71.2%
multimodal-object-detection-by-channel35.8%-
multispectral-fusion-for-object-detection-72.4%