InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images
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, Henrique Lott Neto

TL;DR
InsPLAD is the first large-scale real-world dataset and benchmark for power line asset inspection using UAV images, enabling advancements in automated defect detection and classification in the power industry.
Contribution
The paper introduces InsPLAD, a comprehensive dataset with annotated power line assets and defects, and evaluates current computer vision methods on this new benchmark.
Findings
State-of-the-art methods show varied performance across tasks.
InsPLAD presents diverse real-world challenges for computer vision.
Benchmark results highlight areas for future research improvement.
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Infrastructure Maintenance and Monitoring · Industrial Vision Systems and Defect Detection
MethodsNesT
