Unmanned Aerial Vehicle Uses Multiple Sensors for Target Recognition and Classification

DOI

10.2991/978-94-6463-540-9_51How to use a DOI?

Keywords

UAV; Sensor; Target Recognition; Classification; Information Fusion

Abstract

Based on the gradual popularization of UAV and the development trend of UAV application, the application of UAV sensors for target recognition and information fusion analysis has become one of the key topics of today’s research. Researchers have made progress in UAV target recognition by adopting wireless sensor networks and distributed beamforming methods, as well as using YOLO detection method, constructing detection data sets, and extracting target multi-receptive field features by using res2net.By synthesizing the existing literature, this study identifies the strengths and weaknesses of the method, thereby facilitating advancements in science and technology related to UAV application sensors for target recognition and information fusion analysis. Unmanned aerial vehicles (UAVs) use distributed beamforming technology as a result of their integration with wireless sensor networks (WSNs). As a result, it is imperative to enhance data collecting and monitoring, as well as optimize UAV data collection efficiency. However, data synchronization issues remain, such as the quantization of random errors such as wireless channel distortion and noise. The YOLO approach to object detection is incredibly quick. To enhance the features’ ability to represent many scales, a novel hybrid feature pyramid structure is built using the pyramid model as a foundation.Res2net is utilized in this process to extract multi-receptive field features.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mingkai Zhang
PY  - 2024
DA  - 2024/10/16
TI  - Unmanned Aerial Vehicle Uses Multiple Sensors for Target Recognition and Classification
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 512
EP  - 519
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_51
DO  - 10.2991/978-94-6463-540-9_51
ID  - Zhang2024
ER  -