Deep Learning Based Electric Pylon Detection in Remote Sensing Images

In this repository, we try to use deep learning method to detect electronic pylons in remote sensing images. We provide a dataset which contains 1500 remote sensing images. Dateset can be found at https://pan.baidu.com/s/1zCVAmcldxAupEi1d9oe27Q, password is ld2b. The link above is no longer valid, we update our dataset at https://www.kaggle.com/qiaosijia/epd-dataset.

We also offer ten deep learning models which contain Faster R-CNN, Cascade R-CNN, Grid R-CNN, Libra R-CNN, Retinanet, YOLOv3, YOLOv4, Retinanet FreeAnchor, FCOS and Retinanet FSAF.
For time reasons, we uploaded a zip of unpolished code, and the code is still being sorted out. Detailed code and usage details of the code are expected to be uploaded by July 15, 2020.

Our paper has already been included by remote sensing and original text can be found at https://www.mdpi.com/2072-4292/12/11/1857.
If you find our dataset or code useful you can cite us using the following bibTex:

@article{Qiao_2020, title={Deep Learning Based Electric Pylon Detection in Remote Sensing Images}, volume={12}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs12111857}, DOI={10.3390/rs12111857}, number={11}, journal={Remote Sensing}, publisher={MDPI AG}, author={Qiao, Sijia and Sun, Yu and Zhang, Haopeng}, year={2020}, month={Jun}, pages={1857}}