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Artificial Intelligence · Capstone
Traffic Violation Detection & E-Challan System
Flags helmetless and triple riding, signal jumps and drafts a challan
5-6 weeks build
Overview
Junction CCTV footage runs through a YOLOv8 detector for riders, helmets and vehicles, while a virtual stop line tied to the signal state catches red-light jumps. Offending plates are cropped and read with OCR, and each violation lands in an officer dashboard as a draft challan with evidence frames, awaiting human approval before it is issued.
What makes it stand up
- 01Rider, helmet and pillion detection for helmetless and triple riding
- 02Signal-state aware stop line that catches red-light jumping
- 03Plate localisation plus OCR tuned for Indian number plate formats
- 04Evidence bundle per offence: timestamp, frames and plate crop
- 05Officer review queue so no challan issues without a human check
Module breakdown
- 01
Detection
YOLOv8 fine-tuned on riders, helmets, vehicles and plates.
- 02
Rule engine
Zone, line and signal logic that turns detections into offences.
- 03
Plate reading
Crop, deskew and OCR with format validation.
- 04
Challan service
FastAPI backend that drafts challans and stores evidence.
- 05
Officer dashboard
React review queue with approve, reject and export actions.
After this, you will be able to
- Explain how detections become rule-based violations
- Defend why a human approves every challan
- Demo a recorded junction clip producing draft challans
Technology stack
- Python
- YOLOv8
- OpenCV
- EasyOCR
- FastAPI
- React
- Level
- Capstone
- Domain
- Artificial Intelligence
- Build time
- 5-6 weeks
- Difficulty
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