Home▸AI & Machine Learning▸How Computer Vision Learns to Recognise Road Signs

🚦 How Computer Vision Learns to Recognise Road Signs

A 3D walkthrough of how a lightweight CNN such as MobileNetV2 classifies a UK road sign in real time, layer by layer, and how weather, dirt and model size affect confidence.

AI & Machine Learning3DAdvanced60 FPS

A UK road sign passes through a compact convolutional network laid out in 3D — each block shrinking spatially while growing deeper in feature channels, exactly like a MobileNetV2-style backbone used in real driver-assistance cameras.

🔬 What It Demonstrates

Depthwise and pointwise convolution blocks alternate to build up abstract features cheaply, while the classifier at the end turns those features into confidence scores across four UK sign types.

🎮 How to Use

Pick a sign, then degrade the input with weather or dirt and shrink the network with the width slider. Watch the class-score bars shift, and see when the model starts to mis-classify the sign entirely.

💡 Did You Know?

MobileNetV2's depthwise-separable convolutions cut computation by roughly 8-9x compared with standard convolutions, which is why they can run in real time on the modest processors found in production ADAS hardware.

⚙ Under the hood

A 3D walkthrough of how a lightweight CNN such as MobileNetV2 classifies a UK road sign in real time, layer by layer, and how weather, dirt and model size affect confidence.

machine learningcomputer visioncnndeep learningimage recognitionartificial intelligenceneural networksThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install