TY - GEN
T1 - Identification of Safe Landing Areas with Semantic Segmentation and Contour Detection for Delivery UAV
AU - Putranto, Hamid Yusuf
AU - Irfansyah, Astria Nur
AU - Attamimi, Muhammad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Automatic identification of landing area helps UAV (Unmanned Aerial Vehicle) to land correctly and safely. UAV can use computer vision to detect special signs, such as QR code, April tag, and geometric shapes. However, such object detection lacks the ability to fully recognize its surrounding environment. Currently, advances in deep learning have the potential to replace these special markers and can recognize other objects around the landing area more accurately. Moreover, delivery UAV operating in urban areas are prone to uncertain conditions. Thus, this paper proposes a semantic segmentation method to segment objects in the delivery UAV landing area and identify safe landing area. Contour detection is used to extract the segmentation results to get the largest polygon in the contour and coordinate points of the landing target. The process of predicting each image takes 0.52 seconds. This overall method was successfully executed by using an i3 processor with an average processing speed of 4 frames per second. The results showed that the model has a good segmentation ability to identify the target of UAV landing area.
AB - Automatic identification of landing area helps UAV (Unmanned Aerial Vehicle) to land correctly and safely. UAV can use computer vision to detect special signs, such as QR code, April tag, and geometric shapes. However, such object detection lacks the ability to fully recognize its surrounding environment. Currently, advances in deep learning have the potential to replace these special markers and can recognize other objects around the landing area more accurately. Moreover, delivery UAV operating in urban areas are prone to uncertain conditions. Thus, this paper proposes a semantic segmentation method to segment objects in the delivery UAV landing area and identify safe landing area. Contour detection is used to extract the segmentation results to get the largest polygon in the contour and coordinate points of the landing target. The process of predicting each image takes 0.52 seconds. This overall method was successfully executed by using an i3 processor with an average processing speed of 4 frames per second. The results showed that the model has a good segmentation ability to identify the target of UAV landing area.
KW - U-Net
KW - UAV
KW - landing area identification
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85141862721&partnerID=8YFLogxK
U2 - 10.1109/ICITACEE55701.2022.9924113
DO - 10.1109/ICITACEE55701.2022.9924113
M3 - Conference contribution
AN - SCOPUS:85141862721
T3 - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
SP - 254
EP - 257
BT - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
A2 - Prakoso, Teguh
A2 - Riyadi, Munawar Agus
A2 - Arfan, M.
A2 - Soetrisno, Yosua Alvin Adi
A2 - Afrisal, Hadha
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
Y2 - 25 August 2022 through 26 August 2022
ER -