FOD.Vision

AI-Powered Detection

FOD.Vision is an advanced AI software designed to automate the detection and removal of Foreign Object Debris (FOD) from airport surfaces and maintenance hangars. HAIKAI provides both the hardware and software necessary to implement machine vision algorithms, enabling real-time FOD control. 

Real-time Safety powered by AI

FOD.Vision automates the detection, classification, and push notifications directly at the camera level, ensuring immediate response.

Real-time solution

Automated push notifications and geolocation of FOD

Versatile setups

Seamless Deployment

FOD.Vision can be integrated into different vehicles. Equipped with the system, these vehicles send push notifications to operators with the precise location of detected debris on the apron.

Edge Technology
and Data Integrity

Operating in real time without the need for host server support, the system stores all detected FOD data on a dedicated memory card within the HAIKAI compute board, ensuring traceability and secure storage for later consultation.

Improving safety with AI

Enhanced Safety

Real-time FOD detection using AI machine vision minimises the risk of debris-related incidents on runways and taxiways. Key performance indicators (KPIs) such as detection accuracy, precision, and rates of false positives and negatives are tested and benchmarked against current methods.

Operational Efficiency

Automated detection reduces the need for manual inspections, improving operational productivity and reducing downtime. Our goal is to achieve an average error detection rate of less than 10%, with a target of minimising it to less than 5%.

Cost Savings

Timely identification and removal of FOD mitigate potential aircraft damage, reducing maintenance costs and flight delays. We also aim to significantly reduce Full-Time Equivalent (FTE) costs associated with FOD operations.

Enhanced Safety

Real-time FOD detection using AI machine vision minimises the risk of debris-related incidents on runways and taxiways. Key performance indicators (KPIs) such as detection accuracy, precision, and rates of false positives and negatives are tested and benchmarked against current methods.

Operational Efficiency

Automated detection reduces the need for manual inspections, improving operational productivity and reducing downtime. Our goal is to achieve an average error detection rate of less than 10%, with a target of minimising it to less than 5%.

Cost Savings

Timely identification and removal of FOD mitigate potential aircraft damage, reducing maintenance costs and flight delays. We also aim to significantly reduce Full-Time Equivalent (FTE) costs associated with FOD operations.

Measured in live trials in airport environment

> 96%

FOD detected in good visibility

> 90%

FOD detected at night

> 96%

FOD detected with vehicles moving at 50 km/h

Use Cases

The AI-based FOD detection, classification, and notification system automates intelligent FOD control at airports. This solution can be deployed on various platforms, such as airport vehicles, which will operate independently. Equipped with the FOD.Vision system, these vehicles will send push notifications to operators to retrieve the detected FOD at the specified location on the apron. The system operates in real-time without the need for host server support, and all detected FOD data is stored on a dedicated memory card within a HAIKAI compute-board for later consultation and storage.

FOD detection - HAIKAI
FOD detection - HAIKAI

The AI-based FOD detection, classification, and notification system automates intelligent FOD control at airports. This solution can be deployed on various platforms, such as airport vehicles, which will operate independently. Equipped with the FOD.Vision system, these vehicles will send push notifications to operators to retrieve the detected FOD at the specified location on the apron. The system operates in real-time without the need for host server support, and all detected FOD data is stored on a dedicated memory card within a HAIKAI compute-board for later consultation and storage.