TURNAROUNDAI

It is aimed to analyze the ground operations performed on the apron area for scheduled flights in the civil aviation sector with artificial intelligence-based software and image processing technologies by using the existing cameras in the terminal, to monitor the ground operation service processes, to increase their quality and to create warning mechanisms.

Python and its libraries (like Pytorch, OpenCV, CNN) for event detection.

Keypoint Detection

Gstreamer is used in Video Encode and Decode processes.

C++: It is used in the analysis of the detections to machine language on the AI engine side.

Even though solution supports cloud deployment but due to airport’s security concerns for exposing Apron images outside of the airport, solution delivered by on-premises deployment model by using Linux Servers with GPUs.

The level of difficulty for implementation of the solution is depends on the size of the airport and required processes to be captured and analyzed by the system.

Each turnaround service process needs to be trained at each aircraft parking positions So, more parking position and in-scope processes will require additional effort.

Apart from that solution could be deployed straight forward either on-cloud or on-premises, the main effort for the implementation is to built training data for computer vision model.

The main project scope is achieving below given targets with the help of cutting-edge technologies used for computer vision solutions in the market.

Image Processing Technology:

Analysis of images taken by existing cameras for ground operations in the Airport Apron area.

Using image processing algorithms to determine the turnaround processes in areas where aircraft are parked on.

AI-Based Analysis:

Integration of AI-based software for the identification and classification of activities in the ground operations process.

Automatic detection and monitoring of operational processes such as aircraft parking, refueling, loading/unloading.

Turnaround Processes Measurement and Optimization:

Accurately measuring and recording the turnaround times that spent for the aircraft in the Apron area.

Analyzing the data obtained by artificial intelligence and producing suggestions to optimize operational processes.

Monitoring Ground Service Processes:

Real-time monitoring of ground operations via artificial intelligence-based software on existing cameras in the terminal.

Dynamic mapping and monitoring of personnel, equipment and aircraft locations.

Quality Improvements:

Improving the quality of ground operations by using data obtained through artificial intelligence analysis.

Improving business processes and reducing errors with data-based feedback.

Warning Mechanisms:

Establishing automatic warning mechanisms for critical situations or potential problems identified by artificial intelligence algorithms.