What question did this study set out to answer?

The study aims to enhance vehicle target detection in UAV remote sensing by overcoming feature extraction challenges and background interference.

January 25, 2026Journal of Intelligent & Fuzzy Systems

An Efficient and Lightweight Feature Enhancement-Based Algorithm YOLOv11-FEDL for Vehicle Target Detection in Unmanned Aerial Vehicle Remote Sensing

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Authors

YZYue ZhangGuangdong Polytechnic Normal University YZYangyang ZhouLanzhou University of Technology MCMinghua CaoLanzhou University of Technology

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Implication

The YOLOv11-FEDL algorithm improves vehicle target detection in UAVs, enhancing speed and accuracy, suggesting potential in remote sensing applications.

Key Points

The study aims to enhance vehicle target detection in UAV remote sensing by overcoming feature extraction challenges and background interference.
Introduced the FasterNet neural network to improve detection speed and accuracy.
Developed an Efficient Multi-scale Attention (EMA) mechanism for feature fusion.
Integrated DySample and Lightweight Adaptive Extraction (LAE) modules to optimize features.
Evaluated performance on Cardrone and VisDrone2019 datasets.
Achieved mAP@0.5 of 89.7% on Cardrone and 39.1% on VisDrone2019.
Increased inference speed to 301.80 FPS and 334.10 FPS, respectively.
Reduced average inference time to 2.41 ms and 2.43 ms.
Lowered parameter count to 2.33 M and computational complexity to 5.3 GFLOPs.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6975b2eafeba4585c2d6e63d https://doi.org/10.1177/18758967251415453

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Implication

The YOLOv11-FEDL algorithm improves vehicle target detection in UAVs, enhancing speed and accuracy, suggesting potential in remote sensing applications.

An Efficient and Lightweight Feature Enhancement-Based Algorithm YOLOv11-FEDL for Vehicle Target Detection in Unmanned Aerial Vehicle Remote Sensing

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Key Points

Cite This Study

An Efficient and Lightweight Feature Enhancement-Based Algorithm YOLOv11-FEDL for Vehicle Target Detection in Unmanned Aerial Vehicle Remote Sensing

Discussion

Member takes

Implication

Key Points

Cite This Study

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