This repository stores InsPLAD, a dataset introduced in "InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images" IJRS | arXiv. InsPLAD is also used in "Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study" WACV2024 CVF | arXiv.
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.
You can download the dataset here (Google Drive). Labels, when applicable, are in the zip files.
Three datasets in one. In the link above, you will find three zip files:
InsPLAD-det.zipis an Object Detection dataset for Asset detection- InsPLAD-fault folder:
supervised_fault_classification.zipis an Image Classification dataset for Fault Classification of the Assetsunsupervised_anomaly_detection.zipis an Unsupervised Anomaly Detection dataset also for Fault Classification of the Assets
Here is a straightforward workflow that can be applied when using InsPLAD:
The black boxes indicate the function of each sub-dataset in the Power line domain and which Computer Vision task (in parentheses) should be used for each sub-dataset.
- Object Detection dataset
- 17 classes (assets categories)
- 10,607 total images
- 28,933 total instances
- Other properties:
Different bounding box colors mean different classes (not normal/defective objects)
InsPLAD-fault is generated from InsPLAD-det. The annotated objects are cropped and classified into normal/defective.
- Image Classification dataset
- Five assets, 2 to 3 classes each (defect types, e.g., corrosion)
- Other properties in the table above
- Anomaly Detection dataset
- Five assets, 2 classes each (normal or anomalous)
- Other properties in the table above
Normal on top (green frame), and defective at the bottom (red frame)
If you use InsPLAD in your research, please cite it:
@article{doi:10.1080/01431161.2023.2283900,
author = {André Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalhães Simões, Veronica Teichrieb, Michel dos Santos, Hemir Santiago, Virginia Sgotti and Henrique Lott Neto},
title = {InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images},
journal = {International Journal of Remote Sensing},
volume = {44},
number = {23},
pages = {1-27},
year = {2023},
publisher = {Taylor & Francis},
doi = {10.1080/01431161.2023.2283900},
URL = {https://doi.org/10.1080/01431161.2023.2283900},
eprint = {https://doi.org/10.1080/01431161.2023.2283900},
}
@InProceedings{Vieira_2024_WACV,
author = {e Silva, Andr\'e Luiz Vieira and Sim\~oes, Francisco and Kowerko, Danny and Schlosser, Tobias and Battisti, Felipe and Teichrieb, Veronica},
title = {Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2024},
pages = {8246-8255}
}