yuanmaoxun/Awesome-RGBT-Fusion

A collection of deep learning based RGB-T-Fusion methods, codes, and datasets. The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking.

679 64 +0/wk

GitHub

deep-learning feature-fusion multispectral-pedestrian-detection papers rgb-t rgb-t-aerial-object-detection rgbt-crowd-counting rgbt-pedestrian-detection rgbt-salient-object-detection rgbt-semantic-segmentation rgbt-tracking rgbt-vehicle-detection

Trend 3

Star & Fork Trend (20 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 679

forks 64

Growth Velocity

yuanmaoxun/Awesome-RGBT-Fusion has +0 stars this period . 7-day velocity: 0.3%.

Deep analysis is being generated for this repository.

Signal-backed technical analysis will be available soon.

Metric	Awesome-RGBT-Fusion	swin2sr	voicebox-pytorch	mcunet
Stars	679	679	680	677
Forks	64	82	54	106
Weekly Growth	+0	+0	+0	+0
Language	N/A	Python	Python	Python
Sources	1	1	1	1
License	N/A	Apache-2.0	MIT	MIT

Capability Radar vs swin2sr

Awesome-RGBT-Fusion

swin2sr

Maintenance Activity 98

Last code push 11 days ago.

Community Engagement 47

Fork-to-star ratio: 9.4%. Lower fork ratio may indicate passive usage.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 30

No clear license detected — proceed with caution.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.