An improved YOLOv5-based method for UAV object detection

Yunfei Han; Abudoula Aierken; Yi Wang; Yupeng Ma

doi:10.1117/12.3009133

16 October 2023 An improved YOLOv5-based method for UAV object detection

Yunfei Han, Abudoula Aierken, Yi Wang, Yupeng Ma

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032I (2023) https://doi.org/10.1117/12.3009133
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Object detection based on unmanned aerial vehicle (UAV) images is very challenging. The multi-scale size and high density of objects in the UAV view bring great difficulties. To fully address this issue to unleash the potential of UAV applications, the YOLOv5-STD model is proposed. First, we add one more head to locate extremely small object detection by shallow image features; second, we use the attention mechanism to optimize the backbone by the transformer; third, we use SPD-Conv to avoid the loss of fine-grained image feature information. At the last, sufficient experiments on the dataset VisDrone 2022 have proven that the model has good performance, compared with the basic model, the improved model has an average improvement of about 7% in mAP@.5 metrics, and the ablation experiments have verified that its improvement skills have a positive effect on the model. This paper can help developers and researchers get a better experience in the analysis and processing of unmanned aerial vehicle images.

Citation Download Citation

Yunfei Han, Abudoula Aierken, Yi Wang, and Yupeng Ma "An improved YOLOv5-based method for UAV object detection", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032I (16 October 2023); https://doi.org/10.1117/12.3009133

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KEYWORDS

Object detection

Transformers

Head

Convolution

Unmanned aerial vehicles

Education and training

Data modeling

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