Segmentation Damage Tire Instance Segmentation Dataset and Pre-Trained Model by Print

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Explore Dataset

Here are a few use cases for this project:

Tire Manufacturer Quality Control: This model could be used in tire manufacturing facilities to automatically detect and classify damaged tires in the production line, ensuring only high-quality products are distributed.
Automotive Repair and Maintenance: Car repair shops may use this model to automatically scan and determine the type of tire damage, helping them to accurately diagnose problems and suggest necessary repairs to customers.
Road Safety Authorities: The model could be used by road safety authorities and inspection centers to ensure the roadworthiness of vehicles, as part of routine checks or insurance assessments.
Used Car Dealerships: This computer vision model can be useful for used car dealerships to verify the condition of tires in the cars they are selling or buying, enhancing their decision-making process and ensuring the safety of customers.
Autonomous Vehicles: Autonomous vehicles could use this model as part of their on-board systems to monitor tire health in real-time, helping the vehicle identify when it may need tire-related 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{ segmentation-damage-tire_dataset,
    title = { Segmentation Damage Tire Dataset },
    type = { Open Source Dataset },
    author = { Print },
    howpublished = { \url{ https://universe.roboflow.com/print/segmentation-damage-tire } },
    url = { https://universe.roboflow.com/print/segmentation-damage-tire },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jun },
    note = { visited on 2023-12-08 },
}