A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm

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

doi:10.11999/JEIT231170

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3324-3333

PENG Ruihui, LAI Jie, SUN Dianxing, LI Mang, YAN Ruyu, LI Xue. A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3324-3333. doi: 10.11999/JEIT231170

Citation:

PENG Ruihui, LAI Jie, SUN Dianxing, LI Mang, YAN Ruyu, LI Xue. A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3324-3333. doi: 10.11999/JEIT231170

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A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm

doi: 10.11999/JEIT231170 cstr: 32379.14.JEIT231170

PENG Ruihui^{1, 2},
LAI Jie^{2
,
,},
SUN Dianxing^{2, 3},
LI Mang²,
YAN 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

Publish Date: 2024-08-30

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, the proposed algorithm exhibits superior performance in detection accuracy metrics. These findings highlight the practical value of the proposed 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

[1]	SINGH S K, DHAWALE C A, and MISRA S. Survey of object detection methods in camouflaged image[J]. IERI Procedia, 2013, 4: 351–357. doi: 10.1016/j.ieri.2013.11.050.
[2]	王荣昌, 王峰, 任帅军, 等. 基于双流融合网络的单兵伪装偏振成像检测[J]. 光学学报, 2022, 42(9): 0915001. doi: 10.3788/AOS202242.0915001. WANG Rongchang, WANG Feng, REN Shuaijun, et al. Polarization imaging detection of individual camouflage based on two-stream fusion network[J]. Acta Optica Sinica, 2022, 42(9): 0915001. doi: 10.3788/AOS202242.0915001.
[3]	LE T N, NGUYEN T V, NIE Zhongliang, et al. Anabranch network for camouflaged object segmentation[J]. Computer Vision and Image Understanding, 2019, 184: 45–56. doi: 10.1016/j.cviu.2019.04.006.
[4]	FAN Dengping, JI Gepeng, SUN Guolei, et al. Camouflaged object detection[C]. IEEE/CVF Conference On Computer Vision And Pattern Recognition, Seattle, USA, 2020: 2774–2784. doi: 10.1109/CVPR42600.2020.00285.
[5]	FAN Dengping, JI Gepeng, ZHOU Tao, et al. PraNet: Parallel reverse attention network for polyp segmentation[C]. The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention–MICCAI 2020, Lima, Peru, 2020: 263–273. doi: 10.1007/978-3-030-59725-2_26.
[6]	TANKUS A and YESHURUN Y. Convexity-based visual camouflage breaking[J]. Computer Vision and Image Understanding, 2001, 82(3): 208–237. doi: 10.1006/cviu.2001.0912.
[7]	BHAJANTRI N U and NAGABHUSHAN P. Camouflage defect identification: A novel approach[C]. The 9th International Conference on Information Technology, Bhubaneswar, India, 2006: 145–148. doi: 10.1109/ICIT.2006.34.
[8]	ZHANG Wei, ZHOU Qikai, LI Ruizhi, et al. Research on camouflaged human target detection based on deep learning[J]. Computational Intelligence and Neuroscience, 2022, 2022: 7703444. doi: 10.1155/2022/7703444.
[9]	赖杰, 彭锐晖, 孙殿星, 等. 融合注意力机制与多检测层结构的伪装目标检测[J]. 中国图象图形学报, 2024, 29(1): 134–146. doi: 10.11834/jig.221189. LAI Jie, PENG Ruihui, SUN Dianxing, et al. Detection of camouflage targets based on attention mechanism and multi-detection layer structure[J]. Journal of Image and Graphics, 2024, 29(1): 134–146. doi: 10.11834/jig.221189.
[10]	刘珩, 冉建国, 杨鑫, 等. 基于DETR的迷彩伪装目标检测[J]. 现代电子技术, 2022, 45(17): 41–46. doi: 10.16652/j.issn.1004-373x.2022.17.008. LIU Heng, RAN Jianguo, YANG Xin, et al. Camouflage target detection based on detection transformer[J]. Modern Electronics Technique, 2022, 45(17): 41–46. doi: 10.16652/j.issn.1004-373x.2022.17.008.
[11]	YADAV D, ARORA M K, TIWARI K C, et al. Detection and identification of camouflaged targets using hyperspectral and LiDAR data[J]. Defence Science Journal, 2018, 68(6): 540–546. doi: 10.14429/dsj.68.12731.
[12]	HU Jianghua, CUI Guangzhen, and QIN Lie. A new method of multispectral image processing with camouflage effect detection[C]. SPIE 9675, AOPC 2015: Image Processing and Analysis, Beijing, China, 2015: 967510. doi: 10.1117/12.2199206.
[13]	CHENG Xiaolong, GENG Keke, WANG Ziwei, et al. SLBAF-net: Super-lightweight bimodal adaptive fusion network for UAV detection in low recognition environment[J]. Multimedia Tools and Applications, 2023, 82(30): 47773–47792. doi: 10.1007/s11042-023-15333-w.
[14]	FANG Qingyun, HAN Depeng, and WANG Zhaokui. Cross-modality fusion transformer for multispectral object detection[J]. arXiv: 2111.00273, 2021. doi: 10.48550/arXiv.2111.00273.
[15]	MA Jiayi, MA Yong, and Li Chang. Infrared and visible image fusion methods and applications: A survey[J]. Information Fusion, 2019, 45: 153–178. doi: 10.1016/j.inffus.2018.02.004.
[16]	聂茜茜, 肖斌, 毕秀丽, 等. 基于超像素级卷积神经网络的多聚焦图像融合算法[J]. 电子与信息学报, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053. NIE Xixi, XIAO Bin, BI Xiuli, et al. Multi-focus image fusion algorithm based on super pixel level convolutional neural network[J]. Journal of Electronics & Information Technology, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053.
[17]	GEVORGYAN Z. SIoU loss: More powerful learning for bounding box regression[J]. arXiv: 2205.12740, 2022. doi: 10.48550/arXiv.2205.12740.