Research on Improved YOLOv5-based Military Target Recognition Algorithm Used in Complex Battlefield Environment

doi:10.12382/bgxb.2022.0736

Abstract

Abstract:

Military target recognition technology used in complex battlefield environment is the basis and key to improve the battlefield intelligence acquisition capability. A PB-YOLO military target recognition algorithm based on the improved YOLOv5 model is proposed to solve the problems of high missed and false detection rates and poor real-time performance of current military target recognition technology in complex battlefield environments. The improved target recognition algorithm is re-clustered for the identification anchor boxes of military units in the land battlefield to improve the model’s fitness for target size and accelerate the convergence of model, and the channel-spatial parallel attention mechanism is used to increase the model’s attention to the feature information and location information of the targets in complex battlefield environments. BiFPN is used in the feature fusion network part to improve the fusion ability and speed of the model for features, and the Alpha_IoU loss function is used to accelerate the convergence of model, and solve the problem of IoU calculation degradation when the real frame and the predicted frame overlap. The experimental results show that,compared with the mainstream target recognition algorithm, the mAP value obtained by the improved algorithm reaches 90.17% while ensuring the model space complexity under the self-built military target data set., Through the comparison of ablation experiments, the results show that, compared with the original model, the accuracy of the improved network is improved by 11.57%, and it has better recognition performance, which can provide effective technical support for battlefield intelligence acquisition.

Key words: military target recognition, channel-spatial parallel attention mechanism, feature fusion, loss function

CLC Number:

TP391.4

SONG Xiaoru, LIU Kang, GAO Song, CHEN Chaobo, YAN Kun. Research on Improved YOLOv5-based Military Target Recognition Algorithm Used in Complex Battlefield Environment[J]. Acta Armamentarii, 2024, 45(3): 934-947.

Figures/Tables 19

References 28

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参数	数值
lr	0.01
momentum	0.937
weight_decay	0.0005
batch_size	16
image_size	640×640
epoch	300

参数	数值
lr	0.01
momentum	0.937
weight_decay	0.0005
batch_size	16
image_size	640×640
epoch	300

数量及尺寸	坦克	士兵	装甲车	自行火炮
数量/个	1652	2497	1086	1087
尺寸/m	11×3.4×2		5.5×2.5×2.3	8×3×2.1

数量及尺寸	坦克	士兵	装甲车	自行火炮
数量/个	1652	2497	1086	1087
尺寸/m	11×3.4×2		5.5×2.5×2.3	8×3×2.1

序号	先验框宽高值
1	[12.734 28.496]
2	[18.768 39.405]
3	[52.218 42.258]
4	[35.064 83.506]
5	[102.03 61.18]
6	[153.82 115.14]
7	[232.96 306.19]
8	[409.64 177.11]
9	[526.23 396.53]