Defect Detection V1 Computer Vision Project

Benjamin Ho

Updated 2 years ago

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Classes (8)
Additional labelling
Damage Labelling
Dirt Good
Missing
Missing Cover
Missing Labelling
Scratches

Metrics

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Description

Here are a few use cases for this project:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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Cite This Project

LICENSE
CC BY 4.0

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 2024-09-23 },
                            }
                        
                    

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