How to build AI web app to identify electronics components

Have you ever examined a circuit board and wondered what a particular component is? Whether you're learning electronics, repairing a device, or just curious about how a PCB works, being able to quickly identify components is the essential first step.

Here I wanted to share a free, web-based tool I built that uses machine learning AI(Artificial Intelligence) to identify electronic components in real-time through your webcam. No downloads, no installations just open the webpage below link band hold the component up to your camera!

Electronics Component Identification with Webcam AI

AI based web app to identify electronics component

AI based web app to identify electronics component

This tool helps you recognize basic electronic components like resistors, capacitors, diodes, transistors, ICs, and LEDs—simply by holding them up to your webcam or pointing at a PCB. While it won't diagnose faults or tell you if a component is broken, it gives you something just as valuable: knowledge of what you're looking at.

This tool is the foundation for something much bigger. By mastering component identification first, we can eventually build towards:

  • PCB inspection - Scanning entire boards to verify all components
  • Fault detection - Identifying missing, misaligned, or burned components
  • Assembly verification - Checking if the right component is in the right place
  • Reverse engineering - Automatically mapping out circuit boards
  • Repair assistance - Flagging components that look suspicious or out of specification

Technologies Powering Electronics Component Web App

📦 Simplified Tech Stack

LayerTechnology
FrontendHTML, CSS, JavaScript
CameraWebRTC, MediaStream API
AI ModelCustom classifier (Roboflow)
CloudRoboflow Inference API
CommunicationREST API, JSON
HostingAny static web server

How the AI Was Trained

  1. Dataset Creation: Thousands of component images collected and labeled

  2. Annotation: Each image tagged with correct component class

  3. Training: Model trained on Roboflow's infrastructure (GPU-accelerated)

  4. Validation: Tested against unseen images for accuracy

  5. Deployment: Exported to serverless API endpoint

  6. Integration: Connected to web frontend via simple API calls


Post a Comment

Previous Post Next Post