
Visualization of STN-PLAD Dataset in the Deep Lake UI
This STN-PLAD (STN Power Line Assets Dataset) dataset was generated to aid power line companies in detecting high-voltage power line towers in order to avoid putting workers at risk. The dataset contains 5 different annotated object classes such as Stockbridge damper, spacer, transmission tower, tower plate, and insulator. Each object class has an average of 18.1 annotated instances in total images of 133 power lines. The images were taken from various angles, resolutions, backgrounds, and illumination. All the images were taken through a hi-res UAV. This dataset can make use of popular deep object detection methods.
Instead of downloading the STN-PLAD in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.
import deeplake
ds = deeplake.load('hub://activeloop/stn-plad')
Data Fields
- images: tensor containing the image
- images_meta: contains meta information of images
- boxes: a group of tensors holding boxes with information on the categories
- categories: tensor containing categories of Power Line Assets
- super_categories: tensor containing super category of the Power Line Assets
- areas: tensor holding the information of the area of the box
- iscrowds: bool value containing information about whether a certain image should be allowed for training
- date_captured: the date on which the images were collected
- file_name: contains the name of the image
- height: contains the height of the image
- width: contains the width of the image
Train a model on STN-PLAD dataset with PyTorch in Python
dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on STN-PLAD dataset with TensorFlow in Python
dataloader = ds.tensorflow()
- Repository: https://github.com/andreluizbvs/PLAD
- Paper: https://arxiv.org/abs/2108.07944
STN-PLAD Dataset Licensing Information
STN-PLAD Dataset Citation Information
@inproceedings{vieira2021stn,
title={STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images},
author={Vieira-e-Silva, Andr{\'e} Luiz Buarque and de Castro Felix, Heitor and de Menezes Chaves, Thiago and Sim{\~o}es, Francisco Paulo Magalh{\~a}es and Teichrieb, Veronica and dos Santos, Michel Mozinho and da Cunha Santiago, Hemir and Sgotti, Virginia Ad{\'e}lia Cordeiro and Neto, Henrique Baptista Duffles Teixeira Lott},
booktitle={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
pages={215--222},
year={2021},
organization={IEEE}
}