Foreign Object Detection System at Airport Runway based on YOLOv8

Authors

  • Aaron Benny Student, Department of Computer Engineering
  • Mansi Keshattiwar Student, Department of Computer Engineering
  • Eshant Sonune Student, Department of Computer Engineering,
  • Janhavi Upadhye Student, Department of Computer Engineering,
  • Atharva Pawankar Student in Data Analytics Engineering, Northeastern University, Boston(USA)
  • Dr. Sunil M. Wanjari Head of the Department , Department of Computer Engineering, St. Vincent Pallotti College of Engineering & Technology, Nagpur (India)

Keywords:

airports, foreign material, multi-scale feature fusion, object detection

Abstract

With the fast expansion of the global aviation sector, the number and magnitude of airports being built throughout the world is expanding. The job of ensuring aircraft safety has become more difficult in this environment. Any foreign material, trash, or tiny items that occur on the airport runway may represent a major hazard to the aircraft's ground operations safety. As a result, research on FOD detection is crucial. This study presents a detection technique for foreign object debris based on YOLOv8 (You Only Look Once). This technique utilizes a deep residual network to extract features and multi-scale feature fusion to identify small-scale FOD. To validate our proposed technique, sample datasets of foreign object debris are established. Experiments indicate that the YOLOv8-based detection method detects foreign objects and trash with high accuracy and resilience.

References

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Published

29-04-2023

How to Cite

Aaron Benny, Mansi Keshattiwar, Eshant Sonune, Janhavi Upadhye, Atharva Pawankar, & Dr. Sunil M. Wanjari. (2023). Foreign Object Detection System at Airport Runway based on YOLOv8. International Journal for Research Publication and Seminar, 14(3), 127–132. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/481

Issue

Vol. 14 No. 3 (2023): Vol 14 | Issue 3 | NCASIT 2023

Section

Original Research Article

License

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