Runway FOD AI Detection System
Publicada el 2026-07-16
Descripción de la oferta
I need an end-to-end AI solution that lets a runway-patrol drone spot even the tiniest foreign object debris (FOD) in real time. The airframe already carries LiDAR, an infrared sensor and an electro-optical (EO) camera; what is missing is the software that fuses those feeds, classifies debris and reports exact GPS position so ground crews can clear it fast. Scope of detection • Metallic pieces, plastic fragments and loose stones—down to roughly 1 mm in size. • Operation must be reliable in both daylight and nighttime conditions. • Accuracy target is set to a high threshold; false positives have to stay low while recall stays near perfect. Key tasks 1. Build or adapt a multi-sensor fusion pipeline that consumes LiDAR point clouds, IR imagery and EO frames. 2. Train or fine-tune the detection model to differentiate FOD from background, taxiway lights, heat haze, etc. 3. Output a GPS fix (lat/long) for every confirmed object. 4. Provide a lightweight runtime that can run either on the drone’s onboard computer or a nearby edge server with minimal latency. 5. Deliver documented test results on real or simulated runway scenes demonstrating the required accuracy in day and night scenarios. Deliverables • Source code and trained weights (TensorFlow, PyTorch or similar). • Deployment script or container. • Brief technical report summarising methodology, training data used, evaluation metrics and performance. Acceptance criteria When I feed the system a representative data set captured from the drone, it must detect ≥95 % of the target items with <5 % false alarms and return GPS coordinates within a metre of ground truth. If you have previous experience with FOD detection, sensor fusion, LiDAR segmentation or edge-AI optimisation, please highlight it when you respond.
Skills
Fuente original: freelancer