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Open AccessArticle
Lightweight UAV Landing Model Based on Visual Positioning
by
Ning Zhang
Ning Zhang ,
Junnan Tan
Junnan Tan ,
Kaichun Yan
Kaichun Yan and
Sang Feng
Sang Feng *
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(3), 884; https://doi.org/10.3390/s25030884 (registering DOI)
Submission received: 3 January 2025
/
Revised: 27 January 2025
/
Accepted: 30 January 2025
/
Published: 31 January 2025
Abstract
In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV.
Share and Cite
MDPI and ACS Style
Zhang, N.; Tan, J.; Yan, K.; Feng, S.
Lightweight UAV Landing Model Based on Visual Positioning. Sensors 2025, 25, 884.
https://doi.org/10.3390/s25030884
AMA Style
Zhang N, Tan J, Yan K, Feng S.
Lightweight UAV Landing Model Based on Visual Positioning. Sensors. 2025; 25(3):884.
https://doi.org/10.3390/s25030884
Chicago/Turabian Style
Zhang, Ning, Junnan Tan, Kaichun Yan, and Sang Feng.
2025. "Lightweight UAV Landing Model Based on Visual Positioning" Sensors 25, no. 3: 884.
https://doi.org/10.3390/s25030884
APA Style
Zhang, N., Tan, J., Yan, K., & Feng, S.
(2025). Lightweight UAV Landing Model Based on Visual Positioning. Sensors, 25(3), 884.
https://doi.org/10.3390/s25030884
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