pddatasets Object Detection Dataset and Pre-Trained Model by PDDatasets

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Explore Dataset

Here are a few use cases for this project:

Infrastructure Safety Analysis: Engineers and designers can use Pddatasets combined with a computer vision model for automated assessments of roads, buildings, bridges, or other structures to identify potential damage such as cracks and deformations and prioritize areas that need repair.
Geological and Seismic Research: Scientists studying seismic activity could apply this model to images or real-time feed of terrains to identify new or developing geological deformations, which can help in predicting earthquakes or assessing their impact.
Industrial Maintenance: Companies operating large industrial facilities with complex infrastructure, like oil refineries or power plants, can use the model to streamline inspection workflows and identify areas of wear and tear or potential failure.
Pipeline and Drainage System Monitoring: The model can be used for regular inspections of pipelines or sewage systems, helping to early detect any structural deformities or cracks that could lead to leaks, bursts, and failures, which in turn helps in preventive maintenance.
Archaeological Conservation: In the domain of cultural heritage, archaeologists and conservators can use the Pddatasets vision model to inspect monuments, archaeological sites or historical buildings, detecting degradation signs such as cracks or deformities to ensure their proper preservation and maintenance.

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{
                            pddatasets_dataset,
                            title = { pddatasets  Dataset },
                            type = { Open Source Dataset },
                            author = { PDDatasets },
                            howpublished = { \url{ https://universe.roboflow.com/pddatasets/pddatasets } },
                            url = { https://universe.roboflow.com/pddatasets/pddatasets },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-13 },
                            }