This repo stores the STN Power Line Assets Dataset from the paper: STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images.

Download the dataset here (Releases) or here (Google Drive). As for the labels: labels.zip

This paper has been accepted for presentation at SIBGRAPI 2021. arXiv | IEEE.

• Image size: 5472×3078 or 5472×3648
• Total images: 133
• Total instances: 2409
• Average instances per image: 18.1
• Nº of object classes (different assets): 5
• Other stats:

• mAP: 89.2%

Many power line companies are using UAVs to perform their inspection processes instead of putting their workers at risk by making them climb high voltage power line towers, for instance. A crucial task for the inspection is to detect and classify assets in the power transmission lines. However, public data related to power line assets are scarce, preventing a faster evolution of this area. This work proposes the STN Power Line Assets Dataset, containing high-resolution and real-world images of multiple high-voltage power line components. It has 2,409 annotated objects divided into five classes: transmission tower, insulator, spacer, tower plate, and Stockbridge damper, which vary in size (resolution), orientation, illumination, angulation, and background. This work also presents an evaluation with popular deep object detection methods and MS-PAD, a new pipeline for detecting power line assets in hi-res UAV images. The latter outperforms the other methods achieving 89.2% mAP, showing considerable room for improvement.

@INPROCEEDINGS{vieiraesilva2021stn,  
author={{Vieira-e-Silva}, André Luiz Buarque and de Castro Felix, Heitor and de Menezes Chaves, Thiago and Simões, Francisco Paulo Magalhães and Teichrieb, Veronica and dos Santos, Michel Mozinho and da Cunha Santiago, Hemir and Sgotti, Virginia Adélia Cordeiro and Neto, Henrique Baptista Duffles Teixeira Lott},  
booktitle={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},   
title={STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images},   
year={2021},   
pages={215-222},  
doi={10.1109/SIBGRAPI54419.2021.00037}
}