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

• URL: http://arxiv.org/abs/2311.01619v2
• Date: Mon, 4 Dec 2023 01:08:34 GMT
• Title: InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images
• Authors: Andr\'e Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalh\~aes Sim\~oes, Veronica Teichrieb, Michel Mozinho dos Santos, Hemir Santiago, Virginia Sgotti and Henrique Lott Neto
• Abstract summary: 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. 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.
• Score: 1.8524180288472398
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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