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Artificial Intelligence · Major Project
Road Pothole Detection
Detects potholes in road video and pins them on a map
2-3 weeks build
Overview
A phone or dashcam records the road along with GPS, and a YOLOv8 model trained on a public pothole dataset finds potholes frame by frame. Detections are matched to GPS points, nearby duplicates are merged, and each pothole appears on a Leaflet map with a photo and a rough severity from its size, ready to hand to a municipal complaint desk.
What makes it stand up
- 01Pothole detector trained on a public road-damage dataset
- 02GPS track synced with video frames
- 03Duplicate merging so one pothole is one pin
- 04Severity estimate from the size of the detection
- 05Map view with photos and CSV export for complaints
Module breakdown
- 01
Detector
YOLOv8 training with road-condition augmentation.
- 02
Geo-sync
Frame timestamps aligned to a GPS log.
- 03
Deduplication
Distance-based clustering of repeat detections.
- 04
Map
Leaflet view with severity colours and photos.
- 05
Export
CSV and PDF reports per ward or route.
After this, you will be able to
- Explain how you geotag detections from video
- Defend your severity estimate and its limits
- Demo a recorded drive turning into a pothole map
Technology stack
- Python
- YOLOv8
- OpenCV
- Flask
- Leaflet
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 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