TY - GEN
T1 - Data-Augmentation-Based Monocular Visual Obstacle Localization Method for UAV
AU - Wang, Yanjun
AU - Long, Teng
AU - Zhong, Jianxin
AU - Xie, Zeyang
AU - Sun, Jingliang
AU - Zhou, Zhenlin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - To improve the accuracy of obstacle localization, a data-augmentation-based monocular visual obstacle localization method for UAV is proposed in this paper. The method can be divided into two parts: obstacle detection and obstacle localization. In the first part, the obstacle detection algorithm based on the YOLOv5 network is trained. To solve the problem of difficulty in obtaining samples, the data augmentation method is used to simulate different flight environments and camera equipment of UAV. To enhance the accuracy of detection, add penalty terms for the distance between the detection box and the ground truth box center, as well as the aspect ratio of the two boxes, to the loss function of the detection algorithm. In obstacle localization, according to the conversion formula from pixel coordinates to ground coordinates, the location of the obstacle can be achieved with calibrated intrinsic parameters of the camera and the UAV’s attitude. Consequently, UAV can recognize and locate obstacles and take action to avoid collision.
AB - To improve the accuracy of obstacle localization, a data-augmentation-based monocular visual obstacle localization method for UAV is proposed in this paper. The method can be divided into two parts: obstacle detection and obstacle localization. In the first part, the obstacle detection algorithm based on the YOLOv5 network is trained. To solve the problem of difficulty in obtaining samples, the data augmentation method is used to simulate different flight environments and camera equipment of UAV. To enhance the accuracy of detection, add penalty terms for the distance between the detection box and the ground truth box center, as well as the aspect ratio of the two boxes, to the loss function of the detection algorithm. In obstacle localization, according to the conversion formula from pixel coordinates to ground coordinates, the location of the obstacle can be achieved with calibrated intrinsic parameters of the camera and the UAV’s attitude. Consequently, UAV can recognize and locate obstacles and take action to avoid collision.
KW - YOLOv5 network
KW - data augmentation
KW - monocular camera
KW - obstacle detection
KW - obstacle localization
UR - http://www.scopus.com/pages/publications/105000842228
U2 - 10.1007/978-981-96-2216-0_43
DO - 10.1007/978-981-96-2216-0_43
M3 - Conference contribution
AN - SCOPUS:105000842228
SN - 9789819622153
T3 - Lecture Notes in Electrical Engineering
SP - 443
EP - 453
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -