Detect-PCB is a project that uses Computer Vision and Machine Learning technologies to detect issues on Printed Circuit Boards (PCBs). The goal of this project is to apply deep learning methods and image processing techniques to automatically identify defects such as incorrect soldering, missing components, or faulty connections, improving the quality inspection process for PCBs.
This project can be used in automated inspection systems at PCB manufacturing plants, or it can be applied to quality assurance in applications such as IoT devices or consumer electronics.
- Component Detection on PCBs: Identifies components such as pins, solder joints, and other elements on the PCB.
- PCB Fault Diagnosis: Automatically detects issues like missing components, incorrect soldering, or broken connections.
- Easy-to-use Interface: Displays inspection results visually through images or videos, making it easier for users to monitor and recheck.
- Python 3.x (Recommended Python 3.8 or later)
- OpenCV: Library for image processing.
- NumPy: Library for scientific computing.
- TensorFlow or PyTorch (depending on the deep learning model you choose to use).
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Clone the repository:
First, clone the project to your local machine:
git clone https://github.com/DoNguyenAnhTuan/Detect-PCB.git cd Detect-PCB