PCB manufacturing Computer Vision Project

Marcos Boriola

Updated 3 years ago

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Classes (3)
marker
pcb
soldering_iron
Description

Here are a few use cases for this project:

  1. Quality Control in Electronics Manufacturing: The "PCB manufacturing" computer vision model can be used by electronics manufacturers to automate the process of quality assurance. It can identify PCBs, markers, and soldering iron in process, helping to detect any deviations from standard protocol or detect any incorrectly assembled components.

  2. Technical Education and Training: This model can be employed in educational institutions or training programs, which provide courses on electronics and PCB assembly. It can analyze the student's work, identify specific elements and provide feedback based on the accuracy of the assembly process.

  3. Video Tutorial Analysis: The model can be used for analysis of video tutorials related to PCB assembly. It can generate time-stamped tags when each element (like PCB, soldering iron and marker) appears in the video thus helping in content summarization and easy navigation.

  4. Work Safety Monitoring: The model could be used in workshop scenarios to ensure safety compliance. It can identify whether a soldering iron is being used properly and safely during PCB production, avoiding potential worksite accidents.

  5. Automated Reporting and Documentation: The model can be used to automatically document the stages of PCB assembly by capturing and labelling images with pcb, soldering_iron, or marker. This documentation could be used for process transparency, future reference, or case studies.

Supervision

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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-manufacturing_dataset,
                            title = { PCB manufacturing Dataset },
                            type = { Open Source Dataset },
                            author = { Marcos Boriola },
                            howpublished = { \url{ https://universe.roboflow.com/marcos-boriola/pcb-manufacturing } },
                            url = { https://universe.roboflow.com/marcos-boriola/pcb-manufacturing },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { mar },
                            note = { visited on 2024-12-26 },
                            }