PCB_EC-detection Computer Vision Project

PCBCOMPONENTDETECTIONMODEL

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Classes (9)
cap_g
cap_ng
diode_g
diode_ng
ic_g
ic_ng
res_g
res_ng
trans_g

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Description

Here are a few use cases for this project:

  1. Quality Control in Electronics Manufacturing: The "PCB_EC-detection" model can be implemented on production lines to automatically check and classify the electronic components on the PCBs. It can help distinguish between good (g) and no good (ng) parts for components like resistors (res), capacitors (cap), diodes, and integrated circuits (ic).

  2. Automated Sorting and Inventory Management: The model can help in categorizing electronic components into their respective classes in a warehouse or storage facility. This can enable more efficient inventory tracking and management.

  3. Automated Repair & Maintenance: This model can be used in automated diagnostic tools that aid technicians in identifying faulty components in electronic devices. It can pinpoint broken or non-functional components for replacement.

  4. Education and Training: The model can be used as an educational tool for teaching students or hobbyists about the different components on a PCB. It can identify and explain the characteristics of each component to users.

  5. Recycling and Waste Management: The "PCB_EC-detection" model can support in identifying and sorting electronic components for proper recycling or disposal, particularly in distinguishing between reusable (g) and non-reusable components (ng).

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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{
                            pcb_ec-detection_dataset,
                            title = { PCB_EC-detection Dataset },
                            type = { Open Source Dataset },
                            author = { PCBCOMPONENTDETECTIONMODEL },
                            howpublished = { \url{ https://universe.roboflow.com/pcbcomponentdetectionmodel/pcb_ec-detection } },
                            url = { https://universe.roboflow.com/pcbcomponentdetectionmodel/pcb_ec-detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-11-22 },
                            }
                        
                    

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