<![CDATA[Flight safety is a crucial aspect of the aviation industry, heavily reliant on optimal runway conditions for successful takeoffs and landings. Foreign Object Debris (FOD) such as stones and potholes on the runway pose significant threats, necessitating preventive measures to avoid operational failures and aircraft damage. This research focuses on designing an image processing application for drone usage to enhance runway inspections. The Research and Development (R&D) method is employed from application design to field testing. Image processing utilizes the You Only Look Once (YOLO) method for swift and accurate FOD detection, maximizing drone effectiveness in identifying risks. Findings indicate that object size and image capture distance significantly affect detection performance. This analysis provides a basis for model optimization through improved data augmentation techniques and parameter adjustments to address challenges in detecting objects of various sizes.]]>
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