INFRASTRUCTURE · TRANSPORT
INFRASTRUCTURE · TRANSPORT
INFRASTRUCTURE · TRANSPORT
INTELLIGENT SYSTEMS
INFRASTRUCTURE · TRANSPORT
INFRASTRUCTURE · TRANSPORT
AI INNOVATION
INFRASTRUCTURE · TRANSPORT
AI INNOVATION
Infrastructure Monitoring: Smart AI Solutions for Transport
A toll road operator needed a smarter way to detect over-height vehicles before they caused costly infrastructure damage. We designed and deployed a computer vision system that improved detection accuracy by 94%, responded in under 200ms, and reduced manual monitoring hours by approximately 70%, operating 24/7 without fatigue.
94%
<200ms
<200ms
improvement in over-height vehicle detection accuracy
<200ms
<200ms
<200ms
real-time detection latency per video frame
~70%
<200ms
~70%
reduction in manual monitoring hours required
CLIENT
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
Toll Road Operator
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
Intelligent Systems · AI Innovation
INDUSTRY
WHO THIS IS FOR
WHO THIS IS FOR
Infrastructure · Transport & Road Safety
WHO THIS IS FOR
WHO THIS IS FOR
WHO THIS IS FOR
Operations teams managing physical infrastructure and real-time safety monitoring
The Challenge
Manual monitoring couldn't keep pace with real-time risk
Road infrastructure operators have a critical obligation: keep the network safe. For this toll road operator, one of the most persistent risks was over-height vehicles entering tunnels and underpasses, a scenario that can cause serious structural damage and endanger drivers.
The existing approach relied on manual monitoring, staff reviewing camera feeds and responding to alerts. But as traffic volumes grew, that approach showed clear limitations:
- Manual camera review was intermittent and prone to human fatigue, especially on overnight shifts
- Existing detection systems generated frequent false positives, eroding operator confidence
- No automated tracking of vehicle movement once flagged, each incident required manual follow-up
- Scaling the monitoring capability would have required significant additional headcount
- Incident response times were inconsistent, creating variable safety outcomes
The operator wanted to explore whether computer vision and AI could deliver consistent, real-time detection that would scale without proportionally scaling cost.
Our Approach
Feasibility first, then rigorous proof-of-concept delivery
We began with a structured feasibility phase, evaluating the suitability of existing camera infrastructure, assessing available deep learning architectures for object detection, and defining clear performance benchmarks that would determine whether a production deployment was viable.
Stakeholder engagement was built into every phase. Operations managers, safety officers, and technology teams were involved from the outset to ensure the solution would integrate with existing incident response workflows, not create a parallel one.
- Feasibility research and camera infrastructure assessment
- Evaluation and selection of object detection and tracking architectures
- AI solution design optimised for real-time performance constraints
- Development and testing of proof-of-concept with live video feeds
- Stakeholder workshops to align on alert thresholds and response protocols
- Staged deployment with performance monitoring and model refinement
The Solution
A computer vision system built for always-on infrastructure monitoring
We designed and delivered an end-to-end computer vision pipeline capable of detecting and tracking over-height vehicles in real time, across multiple camera feeds simultaneously.
01 Deep learning detection models
01 Deep learning detection models
01 Deep learning detection models
Trained on road infrastructure video data to accurately identify vehicle height anomalies under varying lighting, weather, and traffic conditions.
02 Real-time object tracking
01 Deep learning detection models
01 Deep learning detection models
Tracks flagged vehicles across camera zones, maintaining continuity of detection from point of entry through the monitored corridor.
03 Live video analytics pipeline
03 Live video analytics pipeline
03 Live video analytics pipeline
Processes incoming video streams with sub-200ms latency, enabling alerts to be triggered before a vehicle reaches a critical point in the network.
04 Automated alerting & logging
03 Live video analytics pipeline
03 Live video analytics pipeline
Generates structured incident records for every detection event, supporting compliance reporting and enabling continuous model improvement over time.
The system was built to run continuously without human supervision, escalating only genuine detections to operators, dramatically reducing alert fatigue.
The Outcome
Consistent, scalable safety monitoring — without scaling headcount
The deployed system gave the operator a monitoring capability that works around the clock with no degradation in performance. Staff shifted from reactive monitoring to exception management, only engaging when the system surfaced a genuine incident.
94%
<200ms
<200ms
improvement in over-height vehicle detection accuracy
<200ms
<200ms
<200ms
real-time detection latency per video frame
~70%
<200ms
~70%
reduction in manual monitoring hours required
- 24/7 detection capability with no shift-based gaps or fatigue-related misses
- Dramatically reduced false positive rate, operators trust the alerts they receive
- Consistent incident records created automatically for every detection event
- Monitoring capability now scales with traffic volume, not headcount
- Proof-of-concept validated for full production rollout across the network
What Made This Work
Performance benchmarks, not just technology enthusiasm
Many computer vision projects stall between proof-of-concept and production. Three things kept this one on track.
Clear success criteria from day one
Clear success criteria from day one
Clear success criteria from day one
We defined detection accuracy and latency thresholds before writing a line of code, giving the team an objective bar to hit, not a subjective one to argue about.
Designed for operations, not demos
Clear success criteria from day one
Clear success criteria from day one
The solution was built around the realities of shift work and incident response, not optimised for a controlled demo environment that wouldn't survive contact with live traffic.
Staged rollout with feedback loops
Clear success criteria from day one
Staged rollout with feedback loops
Each deployment phase included structured feedback from operators, allowing the model to be refined on real-world data before scaling across the full network.
Let's explore how AI can help
Whether you operate transport networks, utilities, or built environments, we can help you identify where intelligent systems deliver the most safety and operational value.
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