Infrastructure Intelligence
Road and infrastructure damage detection
Turn dashcam footage and drone imagery into a prioritised repair list, automatically detecting cracks, potholes, and surface damage in real time.
Problem
Road authorities inspect thousands of kilometres manually: slow, expensive, and inconsistent. A single inspector misses hairline cracks that become potholes within a season. By the time damage is reported, repair costs have multiplied. The challenge is a system that processes dashcam or drone imagery in real time, classifies damage by type and severity, and produces a prioritised repair list, turning reactive maintenance into proactive asset management.
System demo
Inference running on SKU-110K public dataset · YOLOv8m pretrained checkpoint · Annotations rendered with OpenCV
Architecture
Dashcam / drone feed
Frame extraction + preprocessing
YOLOv8 damage detection
Damage classification
Severity scoring + GPS tag
Priority repair list
Dashboard / GIS export
MetricsPrototype benchmarks on RDD2022 validation set — to be replaced with production results
mAP@50
73.4%
Precision
76.8%
Recall
71.2%
F1 score
73.9%
Inference (GPU)
38ms
Damage classes
4
Tech stack
Edge deployment — works where connectivity doesn't
Road inspection vehicles and drones operate in areas with no reliable internet. We deploy optimised model variants directly onto onboard hardware, enabling real-time detection and GPS coordinate logging with no cloud dependency. Results sync automatically when connectivity is restored.
Production considerations
- Input source flexibility — dashcam video, drone footage, or static survey images; frame extraction rate configurable per use case
- Weather and lighting variation — augmentation pipeline covers rain, shadow, and low-light conditions across 6 countries in training data
- GPS tagging — detections tagged with coordinates from vehicle or drone telemetry for direct GIS integration
- Severity scoring — damage instances scored by area, class, and density to produce a prioritised repair queue, not just a raw detection list
- GIS / asset management integration — output feeds existing road asset platforms via REST API or GeoJSON export
- Retraining workflow — new road surface types or country-specific damage patterns added with a labelled sample set; built into every delivery
Explore this project
Live demo and source code links will be added as they become available.