
The upkeep of roadways and urban infrastructure is a cornerstone of modern transportation networks. From bollards to traffic signs, street assets ensure safety and guide drivers. However, as illustrated in the examples above, many challenges arise due to damaged, occluded, or dirty assets, and unpredictable conditions like flooding. SmartVision is leading the charge in addressing these issues using advanced computer vision and machine learning technologies. Here’s how SmartVision is reshaping the landscape of street asset management.
The Challenge: Detecting and Monitoring Street Assets
Traditionally, street asset inspections and condition assessments have relied on manual processes. This approach is:
Time-consuming: Inspecting a network of streets is labour-intensive and prone to delays.
Inconsistent: Human error can lead to oversight of critical issues.
Reactive: Problems are often addressed after they become significant hazards.
Modern cities demand a more proactive and efficient system, and SmartVision delivers just that.
How SmartVision Detects and Conditions Street Assets
SmartVision uses state-of-the-art computer vision models trained on vast datasets to:
1. Identify Broken Bollards
Broken bollards, like the ones depicted above, pose safety risks and create visual clutter on roads. SmartVision’s object detection algorithms can:
Identify the precise location of damaged bollards.
Classify them based on the extent of damage.
Prioritise repairs by integrating with maintenance workflows.
2. Detect Occluded or Dirty Signs
Obstructed or dirty road signs can lead to confusion for drivers and increase accident risks. SmartVision excels at:
Identifying partially or fully occluded signs using advanced segmentation techniques.
Highlighting signs affected by dirt, weathering, or overgrown vegetation.
Sending alerts for cleaning or trimming tasks.
3. Recognise Flooded Roads
Flooding is a significant hazard that disrupts traffic and endangers lives. SmartVision leverages its anomaly detection models to:
Detect water levels on roadways through real-time image and video feeds.
Differentiate between puddles and hazardous flooding zones.
Integrate with traffic management systems to issue warnings and reroute traffic.
4. Monitor General Asset Conditions
Beyond detecting specific issues, SmartVision continually monitors street assets’ overall health. This includes identifying:
Faded road markings.
Leaning or damaged poles.
Structural integrity issues in signage or barriers.
The Technology Behind SmartVision
1. Deep Learning Algorithms
SmartVision relies on convolutional neural networks (CNNs) and transfer learning to achieve unparalleled accuracy in detecting and classifying assets. By training on diverse datasets, the system can adapt to different environments and scenarios, such as urban roads, motorways, or rural areas.
2. Edge Computing for Real-Time Analysis
To ensure seamless operation, especially for detecting time-critical issues like flooding, SmartVision integrates edge computing. This enables:
Real-time processing of visual data on-site.
Low latency, which is critical for traffic management and emergency response systems.
3. Integration with GIS and Maintenance Systems
SmartVision’s outputs are seamlessly integrated with Geographic Information Systems (GIS) and maintenance platforms. This ensures:
Geotagged issues for precise repairs.
Automated work order generation for maintenance teams.
Real-World Benefits of SmartVision
1. Improved Road Safety
By proactively addressing damaged bollards, obscured signs, and flooding, SmartVision helps reduce accidents and improve navigation for drivers.
2. Cost Efficiency
Automation cuts down on manual inspections and ensures resources are allocated effectively.
3. Proactive Maintenance
Issues are detected early, reducing the need for costly emergency repairs.
4. Scalability Across Networks
SmartVision can scale to monitor vast road networks, making it suitable for local councils and national transportation agencies alike.
A Future Powered by SmartVision
SmartVision represents a significant leap forward in road asset management. With its ability to detect and condition street assets—from identifying broken bollards and dirty signs to recognising flooded roads—it’s a testament to how AI and computer vision can transform public infrastructure.
As cities continue to expand and infrastructure ages, tools like SmartVision will play a pivotal role in maintaining safe, efficient, and sustainable roadways for everyone. To learn more, visit SmartVision Technology.
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