AI-Powered Foreign Object Detection for Airfields
Real-time detection and localization of foreign objects on runways and taxiways using efficient deep learning models applied to camera and drone video feeds.
The Challenge
Foreign Object Debris (FOD) on runways and taxiways is a serious safety and cost concern in aviation. Loose hardware, fragments, wildlife remains, personal items, and material shed from aircraft can cause catastrophic engine damage, tire blowouts, and structural harm during takeoff and landing. Traditional FOD checks rely on manual visual sweeps — slow, intermittent, and dependent on human attention under varying visibility and weather conditions. Airports need continuous, automated detection that can identify small objects across vast pavement areas in real time.
Our Approach
We developed an efficient object detection system that processes live video feeds from fixed cameras and drone overflights to detect and localize foreign objects on airfield surfaces. The models are optimized for speed and accuracy on edge hardware, enabling real-time alerting without dependence on cloud infrastructure. The system handles diverse lighting conditions, surface textures, and object scales — from small metallic fragments to larger debris — and delivers precise bounding-box localizations that ground crews can act on immediately.
Technologies Used
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