Written by AQ | Oct 14, 2025 3:00:00 PM
- Rapid Construction of Inspection Models Using LLM
- Automated Analysis of Damage and Deterioration Based on On-Site Photo Data
- Centralized Management and Visualization of Analysis Results via Dashboard
For Various Industries Industry-Specific Use Cases
01. Construction & Civil Engineering (Structure Inspection & Deterioration Diagnosis)
- Use Case
- AI automatically detects and classifies cracks and delaminations in tunnels and bridges.
- Quick processing of extensive infrastructure images captured by drones and cameras.
- Link past inspection data with AI analysis results to predict the progression of deterioration.
- Effects
- Reduces missed detections and improves inspection quality compared to visual inspections.
- Significantly shortens inspection time on site and improves work efficiency.
- Enables planned repair work and optimizes maintenance costs.
02.Transportation Infrastructure (Railway Facility Maintenance & Safety Improvement)
- Use Case
- Automatically identifies minor damage to railway structures (viaducts, tracks).
- Immediately shares inspection results via a dashboard to identify urgent areas.
- Regular data collection allows for time-series monitoring of facility health.
- Effects
- Reduces inspection burden on inspectors and enables efficient inspections outside of normal operating hours.
- Early detection of abnormalities reduces the risk of major accidents.
- Supports data-driven decision-making necessary to maintain safety standards.
03.Manufacturing (Equipment Maintenance & Quality Control)
- Use Case
- Detection of cracks, rust, and other defects in operating production line equipment and piping from images.
- Used in product appearance inspections to learn and determine patterns of minor defects and scratches.
- Applies AI for remote inspection of equipment in hazardous or elevated locations, reducing labor costs.
- Effects
- Prevents unexpected equipment failures (downtime) in advance.
- Achieves inspections with consistent quality standards without variability among inspectors.
- Ensures safety of inspectors and establishes an inspection system not reliant on expert knowledge.