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https://github.com/DocF/multispectral-object-detection

Multispectral Object Detection with Yolov5 and Transformer
https://github.com/DocF/multispectral-object-detection

deep-learning object-detection pytorch transformer yolov5

Last synced: 12 days ago
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Multispectral Object Detection with Yolov5 and Transformer

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README 

        
# Multispectral-Object-Detection



[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/cross-modality-fusion-transformer-for/multispectral-object-detection-on-flir)](https://paperswithcode.com/sota/multispectral-object-detection-on-flir?p=cross-modality-fusion-transformer-for)



[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/cross-modality-fusion-transformer-for/pedestrian-detection-on-llvip)](https://paperswithcode.com/sota/pedestrian-detection-on-llvip?p=cross-modality-fusion-transformer-for)



[![New](https://img.shields.io/badge/2021-NEW-brightgreen.svg)](https://github.com/DocF/multispectral-object-detection/)

![Visitors](https://visitor-badge.glitch.me/badge?page_id=DocF.multispectral-object-detection)

[![GitHub stars](https://img.shields.io/github/stars/DocF/multispectral-object-detection.svg?style=social&label=Stars)](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**








**Day Scene**








 

### Overview








## 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



## Run

#### Download the pretrained weights

yolov5 weights (pre-train) 



-[yolov5s] [google drive](https://drive.google.com/file/d/1UGAsaOvV7jVrk0RvFVYL6Vq0K7NQLD8H/view?usp=sharing)



-[yolov5m] [google drive](https://drive.google.com/file/d/1qB7L2vtlGppGjHp5xpXCKw14YHhbV4s1/view?usp=sharing)



-[yolov5l] [google drive](https://drive.google.com/file/d/12OFGLF73CqTgOCMJAycZ8lB4eW19D0nb/view?usp=sharing)



-[yolov5x] [google drive](https://drive.google.com/file/d/1e9xiQImx84KFQ_a7XXpn608I3rhRmKEn/view?usp=sharing)



CFT weights 



-[LLVIP] [google drive](https://drive.google.com/file/d/18yLDUOxNXQ17oypQ-fAV9OS9DESOZQtV/view?usp=sharing)



-[FLIR] [google drive](https://drive.google.com/file/d/1PwEOgT5ZOTjoKT2LpOzvCsxsVgwP8NIJ/view)



#### Change the data cfg

some example in data/multispectral/



#### Change the model cfg

some example in models/transformer/



note!!!   we used xxxx_transfomerx3_dataset.yaml in our paper.



### Train Test and Detect

train: ``` python train.py```



test: ``` python test.py```



detect: ``` python detect_twostream.py```



## Results



|Dataset|CFT|mAP50|mAP75|mAP|

|:---------: |------------|:-----:|:-----------------:|:-------------:|

|FLIR||73.0|32.0|37.4|

|FLIR| ✔️ |**78.7 (Δ5.7)**|**35.5 (Δ3.5)**|**40.2 (Δ2.8)**|

|LLVIP||95.8|71.4|62.3|

|LLVIP| ✔️ |**97.5 (Δ1.7)**|**72.9 (Δ1.5)**|**63.6 (Δ1.3)**|

|VEDAI||79.7 | 47.7  | 46.8

|VEDAI| ✔️ |**85.3 (Δ5.6)**|**65.9(Δ18.2)**|**56.0 (Δ9.2)**|



### LLVIP

Log Average Miss Rate 

|Model| Log Average Miss Rate |

|:---------: |:--------------:|

|YOLOv3-RGB|37.70%|

|YOLOv3-IR|17.73%|

|YOLOv5-RGB|22.59%|

|YOLOv5-IR|10.66%|

|Baseline(Ours)|**6.91%**|

|CFT(Ours)|**5.40%**|



Miss Rate - FPPI curve








#### References



https://github.com/ultralytics/yolov5