Public Infrastructure Registry Management and Inspection Patrol Solutions (Audin AI)

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Public Infrastructure Registry Management and Inspection Patrol Services Driven by AI Analysis Technology

We help customers implement the digitalization of preventative maintenance and daily patrol operations related to public infrastructure in order to achieve cost reductions and operational efficiency improvements.

• Background
• Overview
• Visual Example
• AI-Based Analysis
• Applicable Structures
• Case Studies

Background

The proportion of public infrastructure built during Japan’s rapid growth period that is over 50 years old will increase rapidly over the next 20 years. As the need for preventative maintenance through prompt, adequate repairs increases, rising costs and shrinking availability of the labor pool needed for inspecting and diagnosing Japan’s extensive social infrastructure have become major issues.

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• The aging of infrastructure is expected to rapidly worsen by 2033, and the impact on society will be significant.
  It will become increasingly difficult for our society to maintain its public infrastructure under the current system.

Similarly, the communications infrastructure facilities that underpin the NTT WEST Group’s communications network were built mainly between the 1960s and the 1980s. In 20 years, 85% of these facilities will be more than 50 years old, with the aging of those facilities becoming more critical year by year. Furthermore, due to the decline in the working-age population, the number of maintenance personnel is also rapidly decreasing, making it urgent that we improve the efficiency of our infrastructure maintenance operations and establish new maintenance methods that do not require advanced skill or knowledge.

• The NTT WEST Group believes that its diverse efforts toward digital transformation (DX) focused on achieving more efficient business operations and creating new value combined with the mutual sharing of AI technology and data assets developed through those efforts with infrastructure operators will contribute to finding solutions for broader societal issues.

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Overview

Audin AI is a cloud service for evaluating infrastructure deterioration by combining drive recorder data with structure identification and deterioration diagnosis technology and by standardizing public infrastructure registry management and AI-based inspection accuracy.
The digitalization of infrastructure information promises to improve the efficiency of infrastructure management operations, and AI-based diagnostics can maintain and improve inspection quality.

Problem-Solving

Visual Example

Public infrastructure image data acquired from drive recorders is combined with deterioration diagnosis AI to provide digital data for infrastructure-related structure registries and to provide reports on deterioration analysis results through a portal website.

AI-Based Analysis

AI is used to analyze public infrastructure image data. By identifying road structures and diagnosing their state of deterioration, it lets you see at a glance where deterioration has occurred.

Applicable Structures

Case Studies

Adoption case study①:　Registry management of convex traffic mirrors and guardrails (Fuchu, Hiroshima)

The city of Fuchu in Hiroshima Prefecture has been using Audin AI for registry management of convex traffic mirrors and guardrails.

Adoption case study②:　Ranked evaluation of road surface paint

Based on Audin AI reports, white lines have been classified into three ranks according to the amount of wear.

1. Note: In general, self-driving vehicles are regarded as capable of handling a wear level of up to 80%, so this was used as the ranking criteria, and white lines with a rank of 3 are given priority for repairs.

Rank	Evaluation description	Evaluation criteria	Visual depiction of paint wear rank
Rank 1	Normal	Wear level of 10% or less
Rank 2	Medium wear level	Wear level of 11% to 79%
Rank 3	High wear level	Wear level of 80% or higher

Adoption case study③:　Updating of road sign registry

This customer used Audin AI to update its road sign registry. Not only did Audin AI add signs to the management registry that had been missing, but it was also able to detect signs that had been removed.

Management registry	Analysis results using Audin AI (current)
ー	Audin AI detected signs that were missing from the management registry and helped to add them to the registry.
	Audin AI helped to detect signs that had actually been removed but remained in the management registry in order to delete them from the registry.

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