InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images

ABSTRACT

Power line maintenance and inspection are essential to avoid power supply interruptions, reducing its high social and financial impacts yearly. Automating power line visual inspections remains a relevant open problem for the industry due to the lack of public real-world datasets of power line components and their various defects to foster new research. This paper introduces InsPLAD, a Power Line Asset Inspection Dataset and Benchmark containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. The dataset contains 17 unique power line assets captured from real-world operating power lines. Additionally, five of those assets present six defects: four of which are corrosion, one is a broken component, and one is a bird’s nest presence. All assets were labelled according to their condition, whether normal or the defect name found on an image level. We thoroughly evaluate state-of-the-art and popular methods for three image-level computer vision tasks covered by InsPLAD: object detection, through the AP metric; defect classification, through Balanced Accuracy; and anomaly detection, through the AUROC metric. InsPLAD offers various vision challenges from uncontrolled environments, such as multi-scale objects, multi-size class instances, multiple objects per image, intra-class variation, cluttered background, distinct point-of-views, perspective distortion, occlusion, and varied lighting conditions. To the best of our knowledge, InsPLAD is the first large real-world dataset and benchmark for power line asset inspection with multiple components and defects for various computer vision tasks, with a potential impact to improve state-of-the-art methods in the field. It will be publicly available in its integrity on a repository with a thorough description. It can be found at https://github.com/andreluizbvs/InsPLAD/.

KEYWORDS:

• Power line inspection
• datasets and evaluation
• object detection
• image classification
• anomaly detection
• deep learning

Acknowledgements

The authors acknowledge the financial support of STN - Sistema de Transmissão Nordeste S.A. through the ANEEL R&D Program for the development of the research project entitled: “PD-04825-0006/2019: Inspeção com Drones por Meio do Acoplamento Eletrostático para Carregamento de Baterias em Voo e Uso de Aprendizagem de Máquina para Classificação Automática de Defeitos”.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/preview/5n3fjgvfyz?a=f68efe15-61d3-4a74-bc8a-d009e3cd3f95.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by STN - Sistema de Transmissão Nordeste S.A. through the ANEEL R&D Program for the development of the research project under grant code PD-04825-0006/2019; Coordenação de Aperfeiçoamento de Pessoal de Ní­vel Superior - Brasil (CAPES) under Finance Code 001; and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).