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.

Bare PCB defect detection using Image Subtraction technique, implemented with OpenCV in Python.

Modelling & Training for a AI-Driven PCB Fault Detection project.

High-precision deep learning ensemble pipeline for PCB defect classification (99.80% accuracy)

An industrial-grade automated optical inspection (AOI) system for Printed Circuit Boards (PCBs). Features a computer vision pipeline for precise defect localization, a fine-tuned EfficientNetB0 model achieving 97.8% classification accuracy, and a full-stack Streamlit dashboard with real-time analytics, batch processing, and automated PDF reporting.

YOLOv8x-HICAUps integrates a HorNet backbone, Convolutional Block Attention Module (CBAM), and attention-based up-sampling to improve feature extraction, feature fusion, and small-object detection accuracy while maintaining computational efficiency.

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.

A Flutter UI for AI-Driven PCB Fault Detection, with Rust+Ort used for the ML inference on edge.

PCB AI Inspector. PCB AI 缺陷检测. AI-powered PCB defect detection system based on YOLO11, supporting real-time inspection & static triggered detection.

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.

🛠️ Detect and classify PCB defects automatically using YOLO11 for efficient quality control and enhanced manufacturing processes.

Automated surface-mount PCB defect detection

> Real-time microscopic defect detection pipeline for Printed Circuit Boards (PCB) using YOLOv8-Nano. Built for Automated Optical Inspection (AOI) in smart manufacturing.

YOLOv8-based PCB defect detection and classification system using deep learning.

Automated PCB defect inspection system using YOLOv8 for real-time detection, localization, and classification of manufacturing defects, with evaluation and failure analysis for industrial deployment.