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Design of Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm

Hongjuan Cai et al.

International Journal of Intelligent Information Technologies2025https://doi.org/10.4018/ijiit.393274article
ABDC C
Weight
0.37

What the paper says

This paper designs an UAV image intelligent recognition system based on a deep learning algorithm, adopts a convolutional neural network as the core architecture, and selects the YOLOv11 (You Only Look Once version 11) model to realize rapid detection and precise classification of targets in images collected by UAVs. The system applies a model lightweight technology to ensure real-time performance and a transfer learning strategy. The model is pre-trained based on ImageNet and COCO (Common Objects in Context) large-scale datasets and is fine-tuned to adapt to UAV aerial image data, improving the model's generalization ability and cross-scene adaptability under small sample conditions. Experimental results demonstrate that the system can still maintain high recognition accuracy and real-time performance in complex terrain and multi-class target environments. The average accuracy value is equal to 84.7% when IoU (Intersection over Union) ≥ 0.5, and the inference time is between 30-35 milliseconds. It has good practical value and promotion prospects.

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https://doi.org/https://doi.org/10.4018/ijiit.393274

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@article{hongjuan2025,
  title        = {{Design of Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm}},
  author       = {Hongjuan Cai et al.},
  journal      = {International Journal of Intelligent Information Technologies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijiit.393274},
}

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Design of Unmanned Aerial Vehicle Image Intelligent Recognition System Based on Machine Learning Algorithm

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.