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Multispectral Object Detection with Yolov5 and Transformer
| Date | Stars |
|---|---|
| 2026-07-24 | 474 |
| 2026-07-25 | 474 |
| 2026-07-28 | 474 |
| 2026-07-30 | 474 |
| 2026-08-06 | 474 |
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# Multispectral-Object-Detection
[](https://paperswithcode.com/sota/multispectral-object-detection-on-flir?p=cross-modality-fusion-transformer-for)
[](https://paperswithcode.com/sota/pedestrian-detection-on-llvip?p=cross-modality-fusion-transformer-for)
[](https://github.com/DocF/multispectral-object-detection/)
[](https://github.com/DocF/multispectral-object-detection)
## Intro
Official Code for [Cross-Modality Fusion Transformer for Multispectral Object Detection](https://arxiv.org/abs/2111.00273).
Multispectral Object Detection with Transformer and Yolov5
## Abstract
Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust in the open world.
To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper.
Unlike prior CNNs-based works, guided by the Transformer scheme, our network learns long-range dependencies and integrates global contextual information in the feature extraction stage.
More importantly, by leveraging the self attention of the Transformer, the network can naturally carry out simultaneous intra-modality and inter-modality fusion, and robustly capture the latent interactions between RGB and Thermal domains, thereby significantly improving the performance of multispectral object detection.
Extensive experiments and ablation studies on multiple datasets demonstrate that our approach is effective and achieves state-of-the-art detection performance.
### Demo
**Night Scene**
<div align="left">
<img src="https://github.com/DocF/multispectral-object-detection/blob/main/video/demo1.gif" width="600">
</div>
**Day Scene**
<div align="left">
<img src="https://github.com/DocF/multispectral-object-detection/blob/main/video/demo.gif" width="600">
</div>
### Overview
<div align="left">
<img src="https://github.com/DocF/multispectral-object-detection/blob/main/cft.png" width="800">
</div>
## Citation
If you use this repo for your research, please cite our paper:
```
@article{qingyun2022cross,
title={Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery},
author={Qingyun, Fang and Zhaokui, Wang},
journal={Pattern Recognition},
volume={130},
pages={108786},
year={2022},
publisher={Elsevier}
}
@article{fang2021cross,
title={Cross-Modality Fusion Transformer for Multispectral Object Detection},
author={Fang Qingyun and Han Dapeng and Wang Zhaokui},
journal={arXiv preprint arXiv:2111.00273},
year={2021}
}
```
## Installation
Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7 (The same as yolov5 https://github.com/ultralytics/yolov5 ).
#### Clone the repo
git clone https://github.com/DocF/multispectral-object-detection
#### Install requirements
```bash
$ cd multispectral-object-detection
$ pip install -r requirements.txt
```
## Dataset
-[FLIR] [[Google Drive]](http://shorturl.at/ahAY4) [[Baidu Drive]](https://pan.baidu.com/s/1z2GHVD3WVlGsVzBR1ajSrQ?pwd=qwer) ```extraction code:qwer```
A new aligned version.
-[LLVIP] [download](https://github.com/bupt-ai-cz/LLVIP)
-[VEDAI] [download](https://downloads.greyc.fr/vedai/)
You need to convert all annotations to YOLOv5 format.
Refer: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
#Excerpt of 5,857 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1862e603290d40ac, topic:deep-learning, topic:pytorch
matched fp:1862e603290d40ac, topic:transformer
matched fp:1862e603290d40ac, topic:object-detection, name:object detection, desc:object detection