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

André Luiz Buarque Vieira e Silva; Heitor de Castro Felix; Franscisco Paulo Magalhães Simões; Veronica Teichrieb; Michel Mozinho dos Santos; Hemir Santiago; Virginia Sgotti; Henrique Lott Neto

doi:10.1080/01431161.2023.2283900

arXiv · 2311.01619

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

André Luiz Buarque Vieira e Silva·Heitor de Castro Felix·Franscisco Paulo Magalhães Simões·Veronica Teichrieb·Michel Mozinho dos Santos·Hemir Santiago·Virginia Sgotti·Henrique Lott Neto

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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 seventeen 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.

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André Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalhães Simões, Veronica Teichrieb, Michel Mozinho dos Santos, Hemir Santiago, Virginia Sgotti, Henrique Lott Neto. 2023-12-04. InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images. https://doi.org/10.1080/01431161.2023.2283900

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