Analysis on the Application of Image Processing in UAV and Autopilot
DOI:
https://doi.org/10.54097/9eth8s35Keywords:
UAV, Image Processing, AutopilotAbstract
The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.
Downloads
References
Liu, D., Pu, G. , & Wu, X. (2022). Quaternion-based improved cuckoo algorithm for colour uav image edge detection. IET image processing, 2022(3), 16.
Aljehani, M. , & Inoue, M. (2019). Performance evaluation of multi-uav system in post-disaster application: validated by hitl simulator. IEEE Access, 2019(99), 1-1.
Jiwen, L. (2019). Application of uav photogrammetry and 3d modeling in mine geological environment monitoring. ACTA GEOLOGICA SINICA (English edition), 93(2), 437-438.
Zhou, H, Peng, J. , Liao, C. , & Li, J. (2020). Application of deep learning model based on image definition in real-time digital image fusion. Journal of Real-Time Image Processing, 17(3),99.
Barbosa, B. D. S., Gabriel Araújo e Silva Ferraz, Santos, L. M. D. , Santana, L. S. , & Conti, L. (2021). Application of rgb images obtained by uav in coffee farming. Remote Sensing, 13(12), 2397.
Tsouros, D. C., Bibi, S. , & Sarigiannidis, P. G. (2019). A review on uav-based applications for precision agriculture. Information (Switzerland), 10(11), 349.
Feito, F. R. (2020). Multispectral mapping on 3d models and multi-temporal monitoring for individual characterization of olive trees. Remote Sensing, 2020(07), 12.
Horstrand, P, Guerra, R. , Rodriguez, A. , Diaz, M. , & Lopez, J. F. (2019). A uav platform based on a hyperspectral sensor for image capturing and on-board processing. IEEE Access, 2019 (99), 1-1.
Wang, L. , & Li, J. (2021). Fast blur detection algorithm for uav crack image sets. Journal of computing in civil engineering, 2021(6), 35.
Shi, Q. , Zhang, J. , & Yang, M. (2021). Curvature adaptive control based path following for automatic driving vehicles in private area. Journal of Shanghai Jiaotong University (Science), 26(5), 690-698.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution 4.0 International License.