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Multimodal-FFM-TLD

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Attention-based Multimodal Image Feature Fusion Module for Transmission Line Detection

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# Multimodal-FFM-TLD
This repository provides a PyTorch implementation of ["Attention-based Multimodal Image Feature Fusion Module for Transmission Line Detection"](https://ieeexplore.ieee.org/abstract/document/9699431), which is accepted by IEEE Transactions on Industrial Informatics.

If you use this code, please cite the paper.
```
@article{choi2022attention,
  title={Attention-based Multimodal Image Feature Fusion Module for Transmission Line Detection},
  author={Choi, Hyeyeon and Yun, Jong Pil and Kim, Bum Jun and Jang, Hyeonah and Kim, Sang Woo},
  journal={IEEE Transactions on Industrial Informatics},
  year={2022},
  publisher={IEEE}
}
```

Overall process of data collection and model inference:
<p align="center">
<img src= "main_fig.png" style="width:90%;">
</p>

## Data set 
We constructed the `Visible Light and Infrared Transmission Line Datset (VITLD)`. The dataset is available at [https://bit.ly/3FBYjBY](https://bit.ly/3FBYjBY).

## Pre-trained Models
UNet [1] with Early Fusion (EF) method [2] of our paper: 
- [Unet_original_4c](https://drive.google.com/file/d/1ni79hc3q24M9OSzc9FnXZ9WglqAS9QtZ/view?usp=sharing)

UNet [1] with proposed feature fusion module (FFM): 
- [Unet_proposed](https://drive.google.com/file/d/1OnYrFX8vKbppSWvb6QpGGcLWikgjjly5/view?usp=sharing)

## Reference
[1] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.

[2] Choi, Hyeyeon, et al. "Real-time power line detection network using visible light and infrared images." International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2019.

Multimodal-FFM-TLD

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