STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images

André Luiz Buarque Vieira-e-Silva; Heitor Felix; Thiago de Menezes Chaves; Francisco Paulo Magalhães Simões; Veronica Teichrieb; Michel Mozinho dos Santos; Hemir da Cunha Santiago; Virginia Adélia Cordeiro Sgotti; Henrique Baptista Duffles Teixeira Lott Neto

arXiv · 2108.07944

STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images

André Luiz Buarque Vieira-e-Silva·Heitor Felix·Thiago de Menezes Chaves·Francisco Paulo Magalhães Simões·Veronica Teichrieb·Michel Mozinho dos Santos·Hemir da Cunha Santiago·Virginia Adélia Cordeiro Sgotti·Henrique Baptista Duffles Teixeira Lott Neto

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Abstract

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 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, showing considerable room for improvement. The STN PLAD dataset is publicly available at https://github.com/andreluizbvs/PLAD.

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André Luiz Buarque Vieira-e-Silva, Heitor Felix, Thiago de Menezes Chaves, Francisco Paulo Magalhães Simões, Veronica Teichrieb, Michel Mozinho dos Santos, Hemir da Cunha Santiago, Virginia Adélia Cordeiro Sgotti, Henrique Baptista Duffles Teixeira Lott Neto. 2021-09-02. STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images. https://arxiv.org/abs/2108.07944

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