Foreign object debris detection on runways by computer vision from drone

The Foreign Object Debris (FOD) is an important topic to discuss in aeronautics as it not only involves safety but also can produce huge economic losses in airports. It is considered FOD any object that should not be in the place it is and as a consequence of this can be a risk to aircrafts or workers. The total cost of FOD to the aerospace industry is calculated to be approximately 4 billion dollars per year. Although new technologies are being implemented, it is a field that still has enhancements to be made. This project is intended to improve the detection of FOD in airports with AI technology applied through a drone with object recognition algorithms that allow to meet the frequencies of inspection and search of operational areas at a low cost of implementation and operation compared to other current detection technologies. The project develops the necessary system architecture to carry out FOD inspections at airports. It comprises studying the requirements, characteristics of the devices involved in the process, and adapting the configuration and parameters of the equipment (drone and camera) to ensure feasibility across all categories of runways. A method for data acquisition, management, and transmission is proposed to determine the position and obtain real-time images of the detected objects. Topics such as the Ground Base Station (GBS), drone tracking, charging station, flight plans, and routes are also addressed. To complete the designed system architecture, an AI model is trained using a FOD database, which will be responsible for obtaining detections during inspections. A new FOD database has also been developed to expand the currently available database. The project's obtained structure is tested through drone flights that simulate real-world applications. The results obtained reveal a functional model with potential for real-world application, but with two current limitations: inspection time and the open nature of FOD. Proposals and new approaches ar
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