Defect Detection V1 Object Detection Dataset and Pre-Trained Model by Benjamin Ho

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Here are a few use cases for this project:

Quality Control in Manufacturing: The Defect Detection V1 model could be deployed on assembly lines to identify faulty products with scratches, dirt, dents, missing labels, or additional unwanted markings, ensuring only items that meet the quality standard are shipped to customers.
Car Rental Services: This model could be used in the vehicle rental industry to inspect returned vehicles for any new scratches, dents, or other damages. This would help in identifying customers who should be charged for repairs.
Resale and Refurbishment Industries: Defect Detection V1 could be utilized by businesses dealing in second-hand or refurbished electronics, furniture, cars, etc., to identify and classify any scratches, dents, missing labels, ensuring the items are refurbished properly before reselling.
Post-Shipping Damage Assessment: Logistics companies could use this model to assess any damage incurred during shipping. The system could take pictures of goods before and after shipping to compare and identify new scratches, dirt, dents, or missing parts, streamlining the process of damage claims.
Commercial Property Leasing: Real estate companies could use this model to document the condition of leased property before and after tenant use, helping to identify potential damage or alterations, streamline the security deposit return process, and maintain the standard of the properties.

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{
                            defect-detection-v1_dataset,
                            title = { Defect Detection V1 Dataset },
                            type = { Open Source Dataset },
                            author = { Benjamin Ho },
                            howpublished = { \url{ https://universe.roboflow.com/benjamin-ho-lzsd2/defect-detection-v1 } },
                            url = { https://universe.roboflow.com/benjamin-ho-lzsd2/defect-detection-v1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { oct },
                            note = { visited on 2024-07-27 },
                            }