LDtrungap-camlo Object Detection Dataset and Pre-Trained Model by QTPC02

Images

2002 images

Explore Dataset

Here are a few use cases for this project:

Power Infrastructure Maintenance: The model can be used to recognize the different components of an electrical power system, making it easier for engineers to identify parts that need maintenance or replacement.
Mapping for Grid Expansion: The model can be used to create a granular map of existing infrastructure, which could be useful for planning grid expansion projects.
Training AI Algorithms in Power Infrastructure: The model could serve as a dataset for training AI algorithms in identifying different parts of the power infrastructure, which could eventually help automate the monitoring process.
Disaster Response & Recovery: After natural disasters, the model may be utilized to analyze aerial or satellite imagery and identify damaged parts of the power infrastructure, allowing for a faster response and recovery.
Safety Compliance: Utility companies could use the model to perform regular safety inspections, ensuring that all components are correctly installed and functioning as intended which ensures compliance with safety regulations.

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{
                            ldtrungap-camlo_dataset,
                            title = { LDtrungap-camlo Dataset },
                            type = { Open Source Dataset },
                            author = { QTPC02 },
                            howpublished = { \url{ https://universe.roboflow.com/qtpc02/ldtrungap-camlo } },
                            url = { https://universe.roboflow.com/qtpc02/ldtrungap-camlo },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-04-28 },
                            }