Webcam Based Foreign Object Debris (FOD) Detection System to Support Airside Inspections with Image Processing - Masyirianti, Jasmin; Hermala, Zusnita; Emiyani, Johny; Hariri, Ahmad (International Journal of Artificial Intelligence Research, 2025)
Ensuring that airside areas are free of Foreign Object Debris (FOD) is a critical activity in aviation, as FOD poses a significant threat to aircraft operations during takeoff and landing. This study aims to enhance FOD detection at Sultan Mahmud Badaruddin II Airport in Palembang by developing an automated detection system that utilizes image processing technology in combination with the YOLO algorithm. Traditional FOD detection methods rely heavily on manual inspections, which are often hampered by blind spots, time constraints, and human error. The proposed webcam-based system significantly improves accuracy and speed in identifying foreign objects, thereby contributing to flight safety by effectively reducing the presence of FOD on runways. By facilitating real-time detection, this research seeks to enhance operational safety, minimize risks of aircraft damage, and reduce costly delays, while also alleviating the burden on personnel. Utilizing the Research and Development (R&D) method based on the Borg and Gall model, the study progresses through seven stages. The developed technology successfully detects four types of FOD with reasonable accuracy: bird carcasses up to 5 meters away, paper up to 3 meters, and both metal and aggregate up to 2 meters. Feedback gathered from surveys distributed to airport technicians reveals a very satisfactory response regarding the device's performance. Ultimately, this technology aims to minimize the risk of FOD-related accidents, ensuring optimal safety for passengers and aircraft alike.