Search Technology for Aircraft Debris Integrating Data Augmentation and Deep Learning Algorithm

Authors

Zhe Yang, Science and Technology on Space Physics Laboratory, Beijing 100076, China
Yinghan Cui, Science and Technology on Space Physics Laboratory, Beijing 100076, China
Lingxi Guo, Science and Technology on Space Physics Laboratory, Beijing 100076, China
Jiaxin Li, Science and Technology on Space Physics Laboratory, Beijing 100076, China
Xusheng Wu, Science and Technology on Space Physics Laboratory, Beijing 100076, China

Abstract

Abstract: The reliable recovery of aircraft debris is of great significance for the complete acquisition of flight test data and the subsequent research and development of models. To ensure the safety of flight tests,the landing area of aircraft experiments is generally an unmanned area,and the actual landing point of the aircraft often deviates from the theoretical landing point. The characteristics of the debris target are complex and the dispersion area is large, making it difficult to search for aircraft debris solely by manpower. Aiming at the difficult problem of aircraft debris recovery in the landing area, through on UAV platforms carrying optical payloads, the research on aircraft debris search technology which integrates data simulation and deep learning algorithms is carried out. The object detection algorithm, data simulation strategy, and application effects of debris search are introduced. Through practical testing, the proposed intelligent search scheme shows good performance, which successfully completes the rapid positioning of aircraft debris in many missions, and ensures the successful completion of flight test missions.

Recommended Citation

Yang, Zhe; Cui, Yinghan; Guo, Lingxi; Li, Jiaxin; and Wu, Xusheng (2024) "Search Technology for Aircraft Debris Integrating Data Augmentation and Deep Learning Algorithm," Journal of System Simulation: Vol. 36: Iss. 10, Article 2.
DOI: 10.16182/j.issn1004731x.joss.24-0864
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss10/2

First Page

2238

Last Page

2245

CLC

TP391.9

Recommended Citation

Yang Zhe, Cui Yinghan, Guo Lingxi, et al. Search Technology for Aircraft Debris Integrating Data Augmentation and Deep Learning Algorithm[J]. Journal of System Simulation, 2024, 36(10): 2238-2245.

Corresponding Author

Wu Xusheng

DOI

10.16182/j.issn1004731x.joss.24-0864