A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm

PENG Ruihui; LAI Jie; SUN Dianxing; LI Mang; YANG Ruyu; LI Xue

doi:10.11999/JEIT231170

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PENG Ruihui, LAI Jie, SUN Dianxing, LI Mang, YANG Ruyu, LI Xue. A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231170

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PENG Ruihui, LAI Jie, SUN Dianxing, LI Mang, YANG Ruyu, LI Xue. A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231170

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A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm

doi: 10.11999/JEIT231170

PENG Ruihui^{1, 2},
LAI Jie^{2
,
,},
SUN Dianxing^{2, 3},
LI Mang²,
YANG Ruyu²,
LI Xue²

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, china
3.
Insitute of Information Fusion, Naval Aeronautical University, Yantai 264001, China

Funds: China Aerospace Science and Technology Corporation Stabilization Support Project (ZY0110020009), The Defense Science and Technology Key Laboratory Fund Project (2023-JCJQ-LB-016)

Received Date: 2023-10-30
Rev Recd Date: 2024-03-24

Available Online: 2024-04-07

Abstract

Abstract

To comprehensively explore the information content of camouflaged target features, leverage the potential of target detection algorithms, and address issues such as low camouflage target detection accuracy and high false positive rates, a camouflage target detection algorithm named CAFM-YOLOv5 (Cross Attention Fusion Module Based on YOLOv5) is proposed. Firstly, a camouflaged target multispectral dataset is constructed for the performance validation of the multimodal image fusion method; secondly, a dual-stream convolution channel is constructed for visible and infrared image feature extraction; and finally, a cross-attention fusion module is proposed based on the channel-attention mechanism and spatial-attention mechanism in order to realise the effective fusion of two different features.Experimental results demonstrate that the model achieves a detection accuracy of 96.4% and a recognition probability of 88.1%, surpassing the YOLOv5 baseline network. Moreover, when compared with unimodal detection algorithms like YOLOv8 and multimodal detection algorithms such as SLBAF-Net, our algorithm exhibits superior performance in detection accuracy metrics. These findings highlight the practical value of our method for military target detection on the battlefield, enhancing situational awareness capabilities significantly.
- Camouflaged target detection,
- Multispectral datasets,
- Attention mechanisms,
- Visible images,
- Infrared images

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References(17)

References

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