%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.02.03.12
%2 sid.inpe.br/sibgrapi/2021/09.02.03.12.10
%@doi 10.1109/SIBGRAPI54419.2021.00037
%T STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images
%D 2021
%A Vieira e Silva, André Luiz Buarque,
%A Felix, Heitor de Castro,
%A Chaves, Thiago de Menezes,
%A Simões, Francisco Paulo Magalhães,
%A Teichrieb, Veronica,
%A dos Santos, Michel Mozinho,
%A Santiago, Hemir da Cunha,
%A Sgotti, Virginia Adélia Cordeiro,
%A Lott Neto, Henrique Baptista Duffles Teixeira,
%@affiliation Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Brazil
%@affiliation Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Brazil
%@affiliation Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Brazil
%@affiliation Departamento de Computação, Universidade Federal Rural de Pernambuco, Brazil
%@affiliation Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Brazil
%@affiliation In Forma Software, Brazil
%@affiliation In Forma Software, Brazil
%@affiliation In Forma Software, Brazil
%@affiliation Sistema de Transmissão Nordeste, Brazil
%E Paiva, Afonso ,
%E Menotti, David ,
%E Baranoski, Gladimir V. G. ,
%E Proença, Hugo Pedro ,
%E Junior, Antonio Lopes Apolinario ,
%E Papa, João Paulo ,
%E Pagliosa, Paulo ,
%E dos Santos, Thiago Oliveira ,
%E e Sá, Asla Medeiros ,
%E da Silveira, Thiago Lopes Trugillo ,
%E Brazil, Emilio Vital ,
%E Ponti, Moacir A. ,
%E Fernandes, Leandro A. F. ,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K object detection, image dataset, inspection, power lines, deep learning, computer vision, uav.
%X 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. The STN PLAD dataset is publicly available at https://github.com/andreluizbvs/PLAD.
%@language en
%3 52.pdf