dienluc Object Detection Dataset and Pre-Trained Model by AISlab

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Explore Dataset

Here are a few use cases for this project:

Power Line Inspection and Maintenance: The "dienluc" model can help utility companies automate the inspection process of power lines by identifying power lines during day or night (day class) and their relative conditions, as well as any anomalies requiring maintenance or repair (su class).
Energy Infrastructure Mapping: The model can be used in mapping power distribution networks, enabling users to better plan and manage energy infrastructure by identifying the locations of power lines (dienluc classes) within satellite or aerial imagery.
Vegetation Management: The model can assist in monitoring vegetation encroachment near power lines, which can help utility companies implement proactive measures for preventing power outages or accidents caused by trees or vegetation interfering with power lines, involving day and su classes.
Disaster Response and Recovery: The "dienluc" model can be employed to assess the extent of damage to power lines during natural disasters like storms, hurricanes, or earthquakes, helping emergency response teams prioritize resources and accelerate repair and recovery efforts.
Wildlife Preservation: The model can help identify power lines that pose a potential threat to animals, particularly birds, allowing for the implementation of bird-safe technologies or other measures to reduce the risk of electrocution and collisions related to power lines.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            dienluc_dataset,
                            title = { dienluc Dataset },
                            type = { Open Source Dataset },
                            author = { AISlab },
                            howpublished = { \url{ https://universe.roboflow.com/aislab/dienluc } },
                            url = { https://universe.roboflow.com/aislab/dienluc },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-04-27 },
                            }