PCB_EC-detection Object Detection Dataset and Pre-Trained Model by PCBCOMPONENTDETECTIONMODEL

Images

740 images

Explore Dataset

Here are a few use cases for this project:

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).
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.
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.
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.
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).

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{
                            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 2024-05-14 },
                            }