Building a Robust PCB Component Detection System with Jetson AI

I am excited to share my latest progress on the DIY Pick N Place project! After several iterations of testing and refinement, I've developed a robust computer vision system capable of detecting and verifying PCB components using NVIDIA's Jetson platform.

Key Features

My system now includes:

• Dual-mode operation supporting both live camera feed and test images
• Robust component detection using SSD-MobileNet v2
• Template matching for placement verification
• Comprehensive error handling and validation
• Built-in support for testing and validation

Technical Implementation

The system is built around a Python class called PCBVisionSystem that handles all aspects of the detection pipeline. I've implemented several key improvements:

First, I added flexible input handling that supports both live camera feeds and test images. This makes it much easier to develop and validate the system using a set of reference PCBs before deploying it in production.

The component detection pipeline uses NVIDIA's optimized deep learning libraries through the Jetson inference API. I am using an SSD-MobileNet v2 model that's been trained to recognize various PCB components including header pins, SMD pads, and IC footprints.

For placement verification, I've implemented a template matching system that compares detected components against reference images. This helps ensure that components are not only detected but correctly oriented and placed.

Usage

The system can be run in two modes:

# Live camera mode
python pcb_vision.py

# Test image mode
python pcb_vision.py --test-image path/to/image.jpg

# Process specific number of frames
python pcb_vision.py --max-frames 100

Next Steps

While the system is now functionally complete, there are several areas I am looking to enhance:

1. Expanding the component template library
2. Adding support for component measurement and tolerance checking
3. Integrating with robotic placement systems

Testing Guidelines

To test the system with your own PCB images:

1. Create a templates directory and add reference images for your components
2. Prepare a set of test PCB images
3. Run the system in test mode with your images
4. Check the detection results and verification scores

CodeBin of Python Code