The PCB Visual Inspection Studio is a Windows-based Automated Optical Inspection (AOI) project that compares a test PCB against a known-good golden reference board and identifies visible differences using classical computer vision techniques.

The system performs image registration, difference analysis, morphological processing, connected-component analysis, and heuristic defect classification to detect potential PCB abnormalities.

Key Features

Golden-board vs test-board comparison
Missing component detection
Misplaced component detection
Solder-region difference detection
Scratch detection
Other visible defect detection
ORB / AKAZE image registration
RANSAC homography alignment
Difference heatmap and defect overlay
Ignore ROI support
Configurable inspection recipes
Webcam / USB camera capture
Inspection history
PASS/FAIL analysis
CSV, JSON, PNG and HTML report export
Built-in synthetic demo boards
Completely offline operation

The inspection pipeline aligns the test image with the reference board, generates a difference map, applies thresholding and morphology, identifies connected components, and assigns heuristic defect labels.

Technologies

Python | PyQt | OpenCV | NumPy | Computer Vision

What You Get

Complete project source code
PCB inspection application
Computer-vision processing modules
Defect classification logic
Camera capture functionality
Recipe and configuration system
Inspection history
Report export functionality
Demo/sample boards
Test suite

Ideal For

B.Tech/M.Tech Projects • Electronics & PCB Projects • Computer Vision Projects • AI/ML Projects • Industrial Automation Projects • Engineering Research

Important Note

This is a classical computer-vision AOI project, not a deep-learning detector. Inspection accuracy depends significantly on consistent lighting, camera positioning, board alignment, and image quality.