Automatic Detection of Foreign Object Debris on Runways

Foreign object debris can easily damage aircraft engines as well as injure personnel in an airport environment. Airfield Inspectors routinely inspect runways for the presence of FOD items, which differ in material, shape and colour using conventional and automated methods. The major shortcoming of the current method is their inability to detect all types of foreign objects in an accurate and timely manner. For removal from the airport runways in this study, we address this shortcoming, that is the lack of accuracy and timelines in the detection by developing an object detection framework to detect FOD for quick removal from the airports. The proposed FOD detection framework consists of unmanned aerial system for inspecting and collecting data from the airfields data processing and augmentation technique to counter the issue of learning on limited types of foreign objects, whether conditions and airport surface materials that are presentin the data sets. A computer vision based object detection model to attain high accuracy and faster interference time force range and develop various models, including the you only look once object detector family of models in this framework.