面向战场低空目标的轻量化识别方法

doi:10.12382/bgxb.2024.0170

摘要/Abstract

摘要：

针对在战场目标识别过程中存在的图像较少、对比度低、资源受限等问题,提出一种名为YOLOF的战场低空目标识别算法。该算法基于YOLOv5s网络模型,使用循环对抗网络应用于图像增强;通过融合RepVGG模块和SiLU激活函数,依靠结构重参数化和更高效的激活函数,提升了模型的特征提取能力和推理速度;再以基于滤波器重要性的剪枝算法,精确评估和删除权重影响较低的滤波器,优化了模型的结构,提升了计算和存储效率;最后通过基于特征层次的知识蒸馏方法,使教师模型向学生模型的特征层进行知识传递,保持了剪枝后模型的高性能。实验结果表明,所提的YOLOF算法相较初始YOLOv5s算法,可以在保证高精度目标识别的同时,实现网络结构的轻量化,即参数量仅有3.6×10⁶,平均精度均值在自制数据集中达到86.3%,已满足战场低空目标的识别要求。

关键词: 目标识别, YOLOv5s网络, 剪枝, 网络轻量化, 知识蒸馏

Abstract:

In the process of intelligent radar target detection,the real-time recognition of targets by edge devices is particularly important.Considering the resource constraints inherent in embedded devices,the need for lightweight target recognition networks has become increasingly prominent.To address the challenges of limited battlefield target images and low contrast,a battlefield low-altitude target recognition algorithm named YOLOF is proposed.This algorithm is based on the YOLOv5s network model and incorporates a cycle-generative adversarial network (CycleGAN) for image enhancement.By integrating the RepVGG module and SiLU activation function,the algorithm enhances the feature extraction capability and inference speed of model through structural reparameterization and more efficient activation functions.Additionally,a pruning algorithm based on filter importance is employed to accurately evaluate and remove the filters with low weight impact,thereby optimizing the model’s structure and improving the computational and storage efficiencies.Furthermore,the knowledge distillation based on feature layers allows the transfer of knowledge from the teacher model to the student model’s feature layers,thus maintaining the high performance of the pruned model.Experimental results demonstrate that the proposed YOLOF algorithm,compared to the original YOLOv5s algorithm,achieves network lightweighting while ensuring high-precision target recognition.Specifically,the parameter count is reduced to just 3.6×10⁶,and the mean average precision (mAP) reaches 86.3% on a custom dataset,meeting the requirements for battlefield low-altitude target recognition.

Key words: target recognition, YOLOv5s network, pruning, network lightweight, knowledge distillation

中图分类号:

TP391.4

徐图南, 高昂, 陈昱成, 闫守成, 邓斌. 面向战场低空目标的轻量化识别方法[J]. 兵工学报, 2025, 46(2): 240170-.

XU Tunan, GAO Ang, CHEN Yucheng, YAN Shoucheng, DENG Bin. A Lightweight Recognition Method for Low Altitude Targets in the Battlefield[J]. Acta Armamentarii, 2025, 46(2): 240170-.

图/表 15

图1 图像增强效果对比

Fig.1 Comparison of image enhancement results

图2 YOLOF网络模型结构图

Fig.2. YOLOF network model structure diagram

图3 SiLU激活函数

Fig.3 SiLU activation function curve

图4 RepVGG训练模块结构图

Fig.4 RepVGG model structure diagram

图5 剪枝具体流程图

Fig.5 Specific workflow diagram of pruning

图6 知识蒸馏流程

Fig.6 Knowledge distillation Workflow

图7 嵌入式计算平台

Fig.7 Structure diagram of of embedded computing platform

表1 二分类样例结果表

Table 1 Predicted results of binary classification samples

真实情况	预测情况
真实情况	正例	反例
正例	TP(真正例)	FN(假反例)
反例	FP(假正例)	TN(真反例)

表2 初始参数设置

Table 2 Initial parameter setting

参数	数值
训练周期	100
批次训练样本数量	16
图片输入大小	640×640
是否断点续训	无
训练冻结层数	0
学习率调整算法	余弦学习率
初始学习率	0.01

图8 目标检测算法性能对比图

Fig.8 Performance comparison of different target detection algorithms

表3 目标检测算法实验结果对比

Table 3 Experimental results of different target detection algorithms

算法	mAP@0.5	F₁	参数量/ 10⁶	模型大小/ MB	浮点运算/ GFLOPs
YOLOv5s	0.865	0.858	7.0	13.7	15.8
YOLOv5x	0.883	0.860	86	165.1	203.8
YOLOv7	0.886	0.860	37.2	71.4	51.2
YOLOv7x	0.887	0.862	70.1	135.2	102.3
YOLOF	0.863	0.861	3.6	7.0	7.1

图9 YOLOF算法检测热力图

Fig.9 YOLOF experimental effect diagram

表4 主流算法实验效果表

Table 4 Experimental effect diagrams of mainstream algorithms

算法	海边	沙地	森林	平原
YOLOv7
YOLOv5s
YOLOv5x
YOLO-F (蒸馏后未剪枝)
YOLO-F

表5 消融实验结果表

Table 5 Chart of ablation experimental results

模型	改进策略	mAP	参数量/10⁶	模型大小/MB
A	Yolov5s(初始模型)	0.865	7.2	13.7
B	A+RepVGG	0.860	6.2	12.6
C	B+剪枝	0.827	3.6	7.0
D	C+知识蒸馏	0.863	3.6	7.0

图10 消融实验性能对比

Fig.10 Comparison chart of ablation experimental performances

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