TY - GEN
T1 - Performance Analysis of YOLOv4 and SSD Mobilenet V2 for Foreign Object Debris (FOD) Detection at Airport Runway Using Custom Dataset
AU - Fairuzi, Muhammad Reza
AU - Zulkifli, Fitri Yuli
N1 - Funding Information:
The author would like to thank Directorate General of Civil Aviation Republic of Indonesia for their collaboration with this research and PT Solusi 247 for their technical support.
Publisher Copyright:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - Indonesia is a country that has heavy air traffic every day. Therefore, safety is a very important thing to pay attention to, one of them is the runway safety. The runway is an important component in aviation activities because aircraft use it for takeoff and landing. Foreign objects or FOD (Foreign Object Debris) could appear on the runway which can cause damage to the aircraft and may result in an accident. Therefore, we need a security system that can detect foreign objects in real-time. One approach that can be done is to use Computer Vision technology by using a camera. This method utilizes Artificial Intelligence (AI) technology for FOD detection. Various methods or algorithms have been developed for Computer Vision, SSD and YOLO are the most frequently used methods for real-time detection because of their high FPS and accuracy performance. Where in this study it was found that SSD MobileNet V2 can reach up to 12 FPS with mAP 0.5 value of 86.8% and for YOLOv4 can reach up to 31 FPS with mAP 0.5 value of 98.73%.
AB - Indonesia is a country that has heavy air traffic every day. Therefore, safety is a very important thing to pay attention to, one of them is the runway safety. The runway is an important component in aviation activities because aircraft use it for takeoff and landing. Foreign objects or FOD (Foreign Object Debris) could appear on the runway which can cause damage to the aircraft and may result in an accident. Therefore, we need a security system that can detect foreign objects in real-time. One approach that can be done is to use Computer Vision technology by using a camera. This method utilizes Artificial Intelligence (AI) technology for FOD detection. Various methods or algorithms have been developed for Computer Vision, SSD and YOLO are the most frequently used methods for real-time detection because of their high FPS and accuracy performance. Where in this study it was found that SSD MobileNet V2 can reach up to 12 FPS with mAP 0.5 value of 86.8% and for YOLOv4 can reach up to 31 FPS with mAP 0.5 value of 98.73%.
KW - Artificial Intelligence
KW - Computer Vision
KW - FOD Detection
KW - SSD MobileNet V2
KW - YOLOv4
UR - http://www.scopus.com/inward/record.url?scp=85127010594&partnerID=8YFLogxK
U2 - 10.1109/QIR54354.2021.9716186
DO - 10.1109/QIR54354.2021.9716186
M3 - Conference contribution
AN - SCOPUS:85127010594
T3 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
SP - 11
EP - 16
BT - 17th International Conference on Quality in Research, QIR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
Y2 - 13 October 2021 through 15 October 2021
ER -