| I&T Solution |
DefectDetect: AI-Powered Roadside Fixture Defect Inspection
(REF: S-1649) |
| Trial Project |
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| Solution Feature |
- AI Computer Vision Technology: Utilize advanced computer vision algorithms to analyze images and videos of roadside fixtures, enabling accurate identification of defects.
- Defective Factor Analysis: Assess deformed factors by comparing the fixture's shape and angle with the ground, and detect corrosion levels using trained models.
- Risk Indicator Consolidation: Combine probabilities from different modules to generate a risk indicator, providing a quantitative assessment of the likelihood of defective fixtures.
- Modular Design: Implement a flexible system architecture that allows for the replacement of modules to serve different video-analytic risk assessment purposes in future stages.
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| Trial Application and Expected Outcome |
- Build model for Identification of roadside fixtures and damage, including Deformed factor analysis and Corrosion detection
- Conduct a trial in a selected area to assess the system's performance in identifying and analyzing defective roadside fixtures accurately.
- Validate the effectiveness of the risk indicator consolidation module in providing actionable insights to authorities for immediate action.
- Demonstrate the potential of the solution to improve public safety by proactively identifying defective roadside fixtures, especially after typhoon or adverse weather situations
- Evaluate the system's scalability and adaptability in different environments and conditions.
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| Additional Solution Information |
Alpha AI_pitch deck_DefectDetect_AIPowered Roadside Fixture Defect Inspection.pdf
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| Info on I&T Solution Provider |
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