Bare PCB defect detection using Image Subtraction technique, implemented with OpenCV in Python.
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Updated
Sep 6, 2022 - Jupyter Notebook
Bare PCB defect detection using Image Subtraction technique, implemented with OpenCV in Python.
Modelling & Training for a AI-Driven PCB Fault Detection project.
A Flutter UI for AI-Driven PCB Fault Detection, with Rust+Ort used for the ML inference on edge.
PCB Defect Detector is a web application designed to analyze and detect defects in printed circuit boards (PCBs). The application leverages modern web technologies and tools to provide an intuitive interface for uploading, analyzing, and visualizing PCB defects. It also includes batch processing, dashboard analytics, and explainable AI insights.
An end-to-end deep learning system for automated PCB defect detection that combines computer vision with domain expertise. This project demonstrates the practical application of AI in industrial quality control, achieving 91.2% F1-score on multi-label defect classification.
This repository contains the code and resources for a PCB defect detection project. The project uses YOLO and other comparative models to detect and classify PCB defects, along with improvements to the dataset for achieving better results.
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