PCB Fault Detection AI-Based Inspection of Printed Circuit Boards

Printed Circuit Boards (PCBs) are essential components of electronic devices, and even minor defects can affect their performance and reliability. Traditional manual inspection is time-consuming and may fail to identify small defects such as missing components, broken copper tracks, soldering errors, and incorrect component placement. To overcome these limitations, the proposed AI-Based PCB Fault Detection System uses computer vision and image processing techniques to analyze PCB images and identify potentially defective regions. The system is developed using Python and OpenCV for image preprocessing and comparison, with an optional machine learning or Convolutional Neural Network (CNN) model for defect detection and classification. The detected results are displayed as a pass/fail status or a possible defect indication, with suspicious regions highlighted wherever supported. This system aims to improve inspection speed, consistency, and quality control while reducing manual effort and rework. However, its reliability depends on image quality, dataset quality, and proper validation