AI Tunnel Maintenance Drone Inspection System
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Solution Overview
Problem
Existing tunnel maintenance methods rely heavily on human visual inspection, which is time-consuming, prone to variability based on inspector skill, and poses safety risks, with a need for a standardized and cost-effective solution for identifying maintenance needs and estimating maintenance costs.
Innovation Solution
A system utilizing a drone to capture tunnel image data with artificial intelligence, dividing the tunnel into photographing areas, selecting dangerous parts for intensive imaging, and calculating maintenance solutions and estimates using big data and AI, including a position signal generating apparatus to determine drone position and a raw material information processing unit for optimal resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human visual inspection is used for tunnel maintenance, then inspection can be performed with simple equipment, but inspection time is excessive and results vary based on inspector skill
Solution Approach 1:
The patent replaces human visual inspection with an automated system comprising a drone equipped with imaging devices and artificial intelligence analysis. The drone captures images of tunnel surfaces, and AI algorithms automatically analyze these images to detect cracks and deterioration, eliminating dependence on human inspectors and significantly reducing inspection time while improving consistency and accuracy.
Solution Approach 2:
The system enables self-inspection through autonomous drone operation and automated AI analysis. The drone independently navigates the tunnel, captures images, and the AI system automatically processes and analyzes the images to identify maintenance needs, reducing the need for manual human intervention in the inspection process.
2Reliability
If comprehensive tunnel inspection is performed to identify all maintenance needs, then maintenance coverage is improved, but inspection cost and complexity increase
Solution Approach 1:
The patent divides the tunnel inspection into segmented processes: the tunnel is divided into multiple sections that the drone inspects sequentially, and the AI analysis is segmented into different detection tasks (crack detection, surface deterioration, etc.). This segmentation allows comprehensive inspection to be achieved through manageable, modular components rather than a single complex system.
Solution Approach 2:
The patent introduces an AI analysis system as an intermediary between image capture and maintenance decision-making. The AI system processes raw images, identifies deterioration patterns, and generates maintenance recommendations, serving as a mediator that translates complex visual data into actionable insights without requiring direct human analysis of every image detail.
3Measurement precision
If detailed image analysis is performed on entire tunnel to find dangerous parts, then detection accuracy is improved, but data processing time increases
Solution Approach 1:
The patent applies preliminary action by first performing a preliminary scan of the entire tunnel to identify potential dangerous areas, then focusing detailed analysis only on those specific regions. The AI system initially processes images at a lower resolution or with simpler algorithms to locate areas of concern, then applies more sophisticated analysis only to those identified zones, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The patent applies local quality by varying the level of analysis applied to different parts of the tunnel. Areas identified as potentially dangerous receive more detailed and sophisticated AI analysis, while normal areas receive lighter processing. This allows the system to concentrate computational resources on critical regions, improving detection accuracy for dangerous parts without proportionally increasing overall processing time.
Data Source
AI summary
Provided are a method and a system for analyzing image data obtained by photographing a tunnel by a drone using artificial intelligence, in tunnel maintenance inspection, rapidly and accurately finding a part that requires maintenance of the tunnel, and calculating a maintenance solution and a maintenance estimate for the part. The system for maintaining a tunnel by analyzing tunnel image data received from a drone using artificial intelligence, includes: the drone that photographs a tunnel to generate the tunnel image data; a position signal generating apparatus that is provided inside the tunnel and generates a position signal for determining position information of the drone in the tunnel; and an artificial intelligence tunnel maintenance apparatus that finds a part of the tunnel that requires maintenance, and calculates an optimal maintenance solution and an optimal maintenance estimate necessary for the tunnel maintenance.