Empty Shelf Detector Computer Vision Project

FYP

Updated 2 years ago

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Description

Here are a few use cases for this project:

  1. Retail Inventory Management: Retailers can integrate the "Empty Shelf Detector" model to automatically monitor their inventory levels in real-time and restock items before they run out. This can greatly improve supply chain efficiency, reduce downtime, and boost customer satisfaction.

  2. Warehouse & Distribution Centers: The model can be used to manage stock in warehouses, ensuring optimal levels are maintained. This can help prevent stockouts, overstock situations, and improve productivity.

  3. Online Marketplace Stock Checking: E-commerce platforms can use this model to keep a real-time track of their stock in the warehouses, thus helping them to update the website for customers accordingly. This can help prevent scenarios where a customer orders an out-of-stock item.

  4. Automated Grocery Stores: Autonomous grocery stores or smart convenience stores, where customers pick up items and are automatically charged when they leave, can apply this model to keep their shelves stocked efficiently.

  5. Vending Machines: Vending machine owners or operators could integrate this model to assess when a particular item is about to get finished and needs restocking. For example, in food or drink vending machines at airports, schools, etc.

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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{
                            empty-shelf-detector-pbyj7_dataset,
                            title = { Empty Shelf Detector Dataset },
                            type = { Open Source Dataset },
                            author = { FYP },
                            howpublished = { \url{ https://universe.roboflow.com/fyp-nrna1/empty-shelf-detector-pbyj7 } },
                            url = { https://universe.roboflow.com/fyp-nrna1/empty-shelf-detector-pbyj7 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-10-10 },
                            }
                        
                    

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