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AMEA-YOLO: a lightweight remote sensing vehicle detection algorithm based on attention mechanism and efficient architecture

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Abstract

Due to the large computational requirements of object detection algorithms, high-resolution remote sensing vehicle detection always involves numerous small objects, high level of background complexity, and challenges in balancing model accuracy and parameter count. The attention mechanism and efficient architecture lightweight-YOLO (AMEA-YOLO) is proposed in this paper. A lightweight network as the backbone network of AMEA-YOLO is designed, and it could maintain model accuracy and ensure good lightweight. FasterNet is employed to accelerate model training speed. The enhanced deep second-order channel attention module (EnhancedSOCA) is utilized to improve the image high-resolution. In addition, a lightweight module is devised to further reduce the model’s weight. The implementation of the HardSwish activation function improves model accuracy. The experimental results indicate that the AMEA-YOLO algorithm could ensure model lightweight and accurate performance.

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Data availability

In this work, the open remote sensing datasets that support the findings of this study are available from the GitHub. VisDrone dataset is available at https://github.com/VisDrone/VisDrone-Dataset. VEDAI dataset is available at https://github.com/nikitalpopov/vedai/tree/master.

Abbreviations

c :

The number of filter

Params:

Parameters

mAP:

Mean average precision

\(|t|_{\textrm{odd}}\) :

The nearest odd number to t

\(C_{\textrm{i}}\) :

The input channels

\(C_{\textrm{o}}\) :

The output channels

H/W:

The size of the output feature map

k :

The size of the one-dimensional convolutional kernel

GFLOPs:

Giga floating-point operations per second

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Acknowledgments

This work is supported by Tianjin Enterprise Science and Technology Commissioner Project (NO.20YDTPJC00170).

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Authors and Affiliations

  1. School of Control and Mechanical, Tianjin Chengjian University, Tianjin, 300384, China

    Shou-Bin Wang, Zi-Meng Gao, Gui-Li Peng & Zi-Jian Yang

  2. STECOL Corporation, Power Construction Corporation of China, Tianjin, 300384, China

    Deng-Hui Jin & Shu-Ming Gong

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  1. Shou-Bin Wang
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  2. Zi-Meng Gao
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Correspondence to Gui-Li Peng.

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Wang, SB., Gao, ZM., Jin, DH. et al. AMEA-YOLO: a lightweight remote sensing vehicle detection algorithm based on attention mechanism and efficient architecture. J Supercomput 80, 11241–11260 (2024). https://doi.org/10.1007/s11227-023-05872-2

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