Infrastructure Fatigue Tracking With Monocular Depth Mapping
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Solution Overview
Problem
Infrastructure inspection and maintenance are often neglected due to the lack of resources and difficulties in routinely inspecting infrastructure, leading to premature degradation and increased maintenance costs.
Innovation Solution
Utilizing monocular depth estimation from crowd-sourced vehicle images to generate detailed depth maps, which are analyzed to identify infrastructure conditions, including fatigue points and cracks, and adjust maintenance schedules using large language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to monitor infrastructure condition, then detailed condition information can be obtained, but the inspection process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system that uses monocular depth estimation algorithms to extract infrastructure condition information from images captured by vehicles, eliminating the need for human inspectors to physically examine the infrastructure
Solution Approach 2:
The system creates digital copies (depth maps) of the infrastructure from monocular images, allowing detailed condition assessment without physical contact or manual measurement, thereby enabling rapid automated analysis of infrastructure health
2Reliability
If comprehensive infrastructure inspection is performed to identify all fatigue points and cracks, then infrastructure safety is improved, but the cost and resources required increase significantly
Solution Approach 1:
The system enables infrastructure to essentially inspect itself by capturing images during normal vehicle operation, with the depth estimation algorithm automatically identifying fatigue points and cracks without requiring specialized inspection equipment or expert human analysis
Solution Approach 2:
The monocular depth estimation system serves multiple functions: it captures infrastructure images, generates depth maps, identifies fatigue points, detects cracks, and monitors deflection, all through a single integrated computational approach that leverages existing vehicle camera systems
3Duration of action of stationary object
If routine maintenance is performed frequently to prevent degradation, then infrastructure longevity is improved, but the time and resources consumed increase
Solution Approach 1:
The system implements continuous feedback by monitoring infrastructure condition over time through repeated image capture and depth map generation, allowing maintenance schedules to be adjusted based on actual condition deterioration rates rather than following fixed preventive maintenance schedules
Solution Approach 2:
The maintenance approach transitions from static, predetermined schedules to dynamic, condition-based scheduling where inspection frequency and maintenance timing are adapted according to the actual structural health status and rate of degradation observed in the infrastructure
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to using monocular depth estimation to derive detailed representations of infrastructure and facilitate condition tracking and maintenance. In one embodiment, a method includes acquiring sensor data about an infrastructure element. The method includes generating depth maps from the sensor data using a depth model that performs monocular depth estimation. The method includes analyzing the depth maps to determine a condition of the infrastructure element. The method includes, responsive to determining that the condition satisfies a health threshold, modifying a maintenance recommendation for the infrastructure element. The method includes providing the maintenance recommendation.