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КыргызчаAI Road Surface Condition CV-Defects, Quality Indices, Prioritization
The AI system analyzes road surfaces using computer vision (CV) to detect defects such as potholes, cracks, and ruts. It generates quality indices that quantify the condition of the pavement, allowing for more accurate prioritization of repair tasks and efficient budget planning.
Cases: Hole/Crack/Rut Detection
The AI system can identify specific types of defects such as holes, cracks, and ruts in the road surface. By analyzing high-resolution images or videos captured by drones or vehicles equipped with sensors, it can pinpoint the exact locations of these issues for targeted repairs.
Quality Indices/Heat-Maps
AI generates quality indices that represent the overall condition of a road surface. These indices are then used to create heat-maps, which visually display areas with poor pavement conditions. Heat-maps help engineers and maintenance teams focus their efforts on the most critical sections first.
Frequently asked questions
What are the plans for repairs and budgets?
Repair plans and budgeting are based on the quality indices generated by the AI system. These indices prioritize repair tasks, ensuring that resources are allocated to areas with the most severe conditions first.
What data is used: video, LiDAR, IoT?
The AI system uses a combination of video and LiDAR data for analysis. Additionally, it can integrate Internet of Things (IoT) sensors to gather real-time information about road conditions.
How do integrations work: CMMS/GIS?
Integrations with Computerized Maintenance Management Systems (CMMS) and Geographic Information Systems (GIS) allow for seamless data transfer, enabling maintenance teams to access detailed repair plans and track progress on a map.
What metrics are used: mAP/ETA/ROI?
The AI system uses metrics such as mean average precision (mAP), estimated time of arrival (ETA), and return on investment (ROI) to evaluate the effectiveness of repair plans and justify budget allocations.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.