This repo contains data pre-processing, classification and defect detection methodologies for images from Advance XRay Inspection from multi-layer PCB boards. Proprietary AXI image dataset is not included in this repo. Users can use their own datasets on top of the OOP data extraction layer and neural network models implemented here.
Explicit errors as values for TypeScript — with a separate defect channel for the unexpected and qualification enforced at every boundary.
Openframeworks version of Local Entropy Filter for image
This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.
🛠️ Detect and classify PCB defects automatically using YOLO11 for efficient quality control and enhanced manufacturing processes.
An impactful project SEM -i-CON which does Defect Classification for Semiconductor Wafer/Die Images from Specialized Electron microscope (SEM) by developing an edge AI
Defect Management website build using React
using Matlab to simulate the graphene energy band with approximation tight binding calculation
The litmus test for a fix — a Claude Code skill that reproduces & proves a defect/fix LIVE in the real app before closing the ticket.
Automatically generate the formation energy diagram for multiple defects. Designed for VASP output.
CNN model for semiconductor wafer defect classification using WM-811K dataset. Achieves 90% accuracy on 8 defect types with class balancing.
Develop a web based app using Streamlit that allows users to upload image of a building structures and to analyze the defects using a Gemini Model
Develop a web based app using Streamlit that allows users to upload image of a building structure and to analyse defects using gemini model
Find defect keys on commit messages, branch names or a custom string
This is based on an Android Studio defect report for an Android Studio emulator defect.