Pcb Defect Recognition Webapp
A Flask-based web app for detecting PCB defects using a trained YOLOv5 model. Features test image analysis, live webcam uploads, and responsive UI with Tailwind CSS. Deployable on Render for global access.
PCB Defect Detection Web App
This is a Flask-based web application designed for detecting defects in printed circuit board (PCB) images using a trained YOLOv5 deep learning model. It offers both static image selection and live webcam upload features. The frontend is styled using Tailwind CSS for a clean and responsive design. The app is deployed using Render, making it accessible from anywhere via the internet.
Features
- 📸 Upload images from a gallery or live webcam
- 🧠 Real-time PCB defect detection using YOLOv5
- 🖼️ Visual output with bounding boxes for detected defects
- 💻 Fully responsive design for both desktop and mobile
- 🌐 Public deployment via Render for easy access and sharing
Technologies Used
- Flask (Python backend)
- YOLOv5 (Deep Learning model)
- HTML, Tailwind CSS, JavaScript
- Render (Hosting/Deployment)
