Tri-Modal Runway Inspection and Abnormality Detection

Project overview

A simulated inspection study comparing sensor and fusion strategies, with explicit limitations on operational use.

A simulated runway-inspection study comparing RGB, thermal and LiDAR detection and fusion strategies. UE5/AirSim samples support reproducible experiments and provenance review. Reported pre-audit diagnostics exposed detection failure, so the work is presented as a research study rather than a deployable inspection system.

Inside this build

• Captured 1,442 simulated samples across 14 conditions with crack and pothole annotations.
• Kept model-based thermal outputs distinguishable from rule-derived detections.
• Compared single-sensor, pairwise and tri-sensor fusion strategies.
• Separated training and held-out sequences, with tuning restricted to training data.
• Estimated uncertainty with sequence-clustered bootstrap analysis.
• Tracked raw artefacts and routing decisions, while recording the limits revealed by evaluation.

Source code & links