3D Infrastructure Asset Models for Automated Signage Inspection
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Solution Overview
Problem
The challenge of efficiently monitoring and maintaining infrastructure assets, particularly signage, is exacerbated by the large scale and geographic extent of these assets, leading to issues such as missed or delayed repairs due to manual inspection processes that are costly and time-consuming, resulting in safety hazards and inconvenience to users.
Innovation Solution
A system utilizing machine learning techniques to detect, geolocate, and assess signage conditions through image analysis, integrating geospatial metadata and 3D point clouds to create an inventory and provide real-time feedback for maintenance, leveraging mobile devices and autonomous vehicles for data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection processes are used to monitor infrastructure assets, then inspection accuracy can be maintained, but inspection time and costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated system using mobile devices, cameras, and machine learning algorithms to detect, geolocate, and assess infrastructure assets. The system automatically captures images, generates 3D point clouds, and identifies asset conditions without human intervention in the field, thereby reducing inspection time while maintaining accuracy through algorithmic analysis.
Solution Approach 2:
The system enables self-service inspection where the infrastructure assets are automatically monitored and assessed by the system itself. Mobile devices captured images are automatically processed through machine learning models that detect signs, assess conditions, and generate maintenance priorities without requiring manual inspection, allowing the system to service itself.
2Reliability
If comprehensive infrastructure asset monitoring is implemented, then safety and maintenance quality improve, but system complexity and implementation costs increase
Solution Approach 1:
The patent creates a universal system that can monitor multiple types of infrastructure assets (signs, pavement, bridges, etc.) using the same mobile device platform and machine learning framework. The system performs multiple functions including image capture, 3D reconstruction, asset detection, condition assessment, and maintenance prioritization within a single integrated platform, reducing overall system complexity through multi-functionality.
Solution Approach 2:
The system creates digital copies of physical infrastructure assets through 3D point clouds and synthetic images generated from mobile device photographs. These digital twins allow for virtual assessment and monitoring without requiring physical inspection, simplifying the monitoring process while improving safety through remote analysis.
3Reliability
If frequent inspections are conducted to ensure timely maintenance, then maintenance quality improves, but resource consumption increases
Solution Approach 1:
The system implements periodic automated inspections using mobile devices that can be deployed at scheduled intervals. The machine learning models continuously analyze new image data and compare it with historical assessments, enabling frequent monitoring at reduced resource cost compared to manual inspections. The system only triggers maintenance alerts when actual changes are detected, optimizing resource utilization.
Solution Approach 2:
The system establishes a feedback loop where inspection results automatically trigger maintenance prioritization and alerts. Machine learning models analyze inspection data and provide feedback on asset conditions, enabling proactive maintenance only when necessary. This feedback mechanism ensures high maintenance quality by responding to actual asset conditions while conserving resources by avoiding unnecessary inspections and maintenance activities.
Data Source
AI summary
Systems and methods for detecting, geolocating, assessing, and/or inventorying infrastructure assets. In some embodiments, a plurality of images captured by a moving camera may be used to generate a point cloud. A plurality of points corresponding to a pavement surface may be identified from the point cloud. The plurality of points may be used to generate at least one synthetic image of the pavement surface, the at least one synthetic image having at least one selected camera pose. The at least one synthetic image may be used to assess at least one condition of the pavement surface.