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
this https URL

Power line maintenance and inspection are crucial to avoid interruptions in the power supply. This paper introduces InsPLAD, a dataset and benchmark for power line asset inspection, containing high-resolution images of power line components and their defects. The dataset evaluates state-of-the-art methods in computer vision tasks related to object detection, defect classification, and anomaly detection.