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Artificial Intelligence · Major Project
Traffic Sign Recognition
Real-time detection and classification for driver assistance
3 weeks build
Overview
A two-stage vision system that detects traffic signs in a video stream and classifies them into 43 categories, running in real time on a laptop CPU after ONNX export, with a dash-cam demo mode built for your presentation.
What makes it stand up
- 01YOLOv8 detector fine-tuned on a traffic sign dataset
- 02Dedicated CNN classifier for fine-grained sign classes
- 03ONNX export with measured CPU latency benchmarks
- 04Video overlay with confidence scores and tracking IDs
- 05Night and motion-blur robustness experiments
Module breakdown
- 01
Detection
Training, anchors, augmentation and mAP evaluation.
- 02
Classification
Crop pipeline and a 43-class CNN.
- 03
Tracking
Frame-to-frame association to steady predictions.
- 04
Optimisation
ONNX conversion and quantisation benchmarks.
- 05
Demo app
Video upload, webcam mode and annotated export.
After this, you will be able to
- Explain mAP and IoU to an examiner
- Justify the two-stage architecture choice
- Show real-time inference on recorded footage
Technology stack
- Python
- YOLOv8
- OpenCV
- PyTorch
- ONNX
- Flask
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3 weeks
- Difficulty
- Rating
- 4.7 / 5 · 126
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support