Apollo Tunnel

    AI-powered tunnel inspection and monitoring with automated defect detection

    Rail tunnel interior with inspection-ready structural surfaces

    Aging tunnels such as the Detroit-Windsor Tunnel face concrete deterioration, water infiltration, deformation, corrosion, poor lighting, high humidity, and vehicle emissions behind steel walls. Manual inspections are labour-intensive, slow, subjective, and can expose inspectors to hazardous environments while critical defects may still go undetected.

    Apollo Tunnel is a non-invasive automated tunnel inspection and monitoring framework using vehicle-mounted LiDAR, cameras, thermal sensors, and GPR. It detects cracks, spalling, leakage, deformation, convergence, and localized rebar issues, then digitizes inspection data for visualization, trend analysis, automated reporting, and maintenance planning.

    AI-Powered Tunnel Intelligence

    AI-driven, image-based tunnel inspection using computer vision for accurate assessments

    Maintenance Planning Support

    Tracks infrastructure changes over time to identify areas requiring maintenance attention before failures occur

    Precise Defect Detection

    Combines computer vision, object detection, and LLMs to assess tunnel health, defects, and deterioration patterns

    Integrating Multiple Data Sources

    Integrates LiDAR, cameras, thermal imaging, GPR, and other sensing inputs to support comprehensive tunnel assessment

    Real-time Collaborative Interaction

    Between field inspection and engineering teams in office to minimize iteration time

    Centralized digital reporting platform

    For digital twin enhancement to improve asset monitoring and scenario analysis

    Extended Asset Lifespan

    Generate clear, structured insights to support faster prioritization and intervention

    AI tunnel defect localization visualization

    Built for Scalable Tunnel Inspection Intelligence

    Examples of AI-classified tunnel inspection imagery

    How Apollo Tunnel Works

    • Ingest inspection imagery from existing tunnel inspection workflows and datasets.
    • Analyze images using computer vision to identify structural components and assess condition.
    • Detect and classify defects using AI models across issues like cracks, corrosion, and delamination.
    • Generate structured insights with clear descriptions to support maintenance and reporting.

    Tunnel Data Input

    High-resolution images of walls, ceilings, beams, and structural elements

    Computer Vision Analysis

    Component classification and condition scoring across tunnel surfaces

    Localization & Defect Detection

    Detection of cracks, corrosion, delamination and staining with precise positioning

    Inspection Report

    Defect logs with severity, classifications, and prioritized maintenance recommendations

    Types of Defects We Inspect & Monitor

    Corrosion tunnel defect example

    Corrosion

    Rust tunnel defect example

    Rust

    Cracks tunnel defect example

    Cracks

    Stains tunnel defect example

    Stains

    Delamination tunnel defect example

    Delamination

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    Validated on real tunnel inspection imagery across varied conditions