Mixed Reality Defect Inspection With AI-Assisted Crack Assessment
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Solution Overview
Problem
Existing infrastructure assessment methods lack a generalized deep learning approach to efficiently identify various types of structural damage, relying heavily on human expertise and manual interaction, which is subjective and time-consuming.
Innovation Solution
A hybrid system combining a mixed reality headset with a deep learning module and user-input actuator, utilizing attention-guided techniques for defect detection and segmentation, allowing for real-time collaboration between human inspectors and AI to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual inspection methods are used, then human expertise can identify defects, but the process is subjective and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical system using a mixed reality headset equipped with a camera and deep learning-based image processing. The system captures images of infrastructure and uses AI algorithms to automatically detect and classify defects, eliminating the need for manual visual inspection while maintaining high detection accuracy.
Solution Approach 2:
The system enables self-service inspection where the infrastructure assessment is performed autonomously by the AI-powered mixed reality system without requiring continuous human intervention. The deep learning model automatically processes images, identifies defects, and provides assessments, allowing the system to serve itself in performing the inspection function.
2Productivity
If automated detection systems are implemented, then inspection time is reduced, but the systems lack the ability to work interactively with human inspectors
Solution Approach 1:
The patent merges automated AI-based defect detection with human inspector expertise by integrating both functions into a single mixed reality system. The AI algorithms automatically process images and identify potential defects, while the mixed reality interface allows human inspectors to interact with the system, verify detections, and provide expert judgment, combining the speed of automation with human intelligence.
Solution Approach 2:
The mixed reality headset serves as an intermediary between the automated detection system and the human inspector. It displays detected defects, allows inspectors to annotate and verify findings, and facilitates collaborative interaction, bridging the gap between automated processing and human expertise.
3Reliability
If deep learning models are used for automated defect detection, then objectivity and accuracy improve, but the system requires extensive training data and computational resources
Solution Approach 1:
The system performs preliminary actions by pre-training deep learning models with extensive defect detection data before deployment. The mixed reality headset captures images that are used to train and refine the AI models in advance, establishing a foundation of objective detection capability that can then be deployed with reduced complexity during actual inspection operations.
Data Source
AI summary
A smart, human-centered technique that uses artificial intelligence and mixed reality to accelerate essential tasks of the inspectors such as defect measurement, condition assessment and data processing. For example, a bridge inspector can analyze some remote cracks located on a concrete pier, estimate their dimensional properties and perform condition assessment in real-time. The inspector can intervene in any step of the analysis/assessment and correct the operations of the artificial intelligence. Thereby, the inspector and the artificial intelligence will collaborate/communicate for improved visual inspection. This collective intelligence framework can be integrated in a mixed reality supported see-through headset or a hand-held device with the availability of sufficient hardware and sensors. Consequently, the methods reduce the inspection time and associated labor costs while ensuring reliable and objective infrastructure evaluation. Such methods offer contributions to infrastructure inspection, maintenance, management practice, and safety for the inspection personnel.