A Fuze Burst Point Detection Method for Outfield Test Images

Yu ZHOU; Ronggang CAO; Ping LI; Xiao MA

doi:10.12382/bgxb.2022.0300

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A Fuze Burst Point Detection Method for Outfield Test Images

更新时间：2025-08-18

- A Fuze Burst Point Detection Method for Outfield Test Images
- Acta Armamentarii Vol. 44, Issue 8, Pages: 2453-2464(2023)
- 作者机构：
  
  1. 北京理工大学机电学院, 北京 100081
  2. 北京理工大学唐山研究院, 河北唐山 063611
  3. 北京理工大学机电动态控制重点实验室, 北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2022.0300
  CLC：
- Received：24 April 2022，
  
  Published Online：06 September 2023，
  
  Published：30 August 2023
- 稿件说明：
移动端阅览
Yu ZHOU, Ronggang CAO, Ping LI, et al. A Fuze Burst Point Detection Method for Outfield Test Images[J]. Acta Armamentarii, 2023, 44(8): 2453-2464.
DOI：

Yu ZHOU, Ronggang CAO, Ping LI, et al. A Fuze Burst Point Detection Method for Outfield Test Images[J]. Acta Armamentarii, 2023, 44(8): 2453-2464. DOI： 10.12382/bgxb.2022.0300.

摘要

在无线电近炸引信抗干扰性能测试中

观测和识别炸点状态对引信的工作状态评估和性能改进至关重要。为此

提出一种基于深度神经网络的图像目标检测算法

用于引信炸点识别。在检测模型的结构设计及训练策略上实现以下新颖设计:模型基于高性能骨干网络ConvNeXt实现图像目标特征提取

使用密集连接跨阶段局部模块以及带通道注意力机制的多分支模块

以提升特征提取能力;使用任务解耦多检测头结构提升检测精度;在模型训练时

使用焦点损失函数作为分类和置信度的损失函数

使用完全交并比函数作为预测框回归的损失函数。研究结果表明:该检测算法在真实引信炸点图像数据集上实现平均精度为92.7%以及F1分数为87.4%的检测性能。实验结果表明

所提算法在引信炸点检测任务上性能优于现有典型检测模型。

Abstract

In the anti-jamming performance test of radio proximity fuzes

the observation and recognition of the burst point state is essential for working state evaluation and performance improvement of the fuzes. Therefore

an image target detection algorithm based on the deep neural network is proposed for fuze burst point recognition. The algorithm achieves the following novel designs in the structure and training strategy of the model: The model realizes the target feature extraction based on the high-performance backbone ConvNeXt

and uses the cross stage partial structure based on dense connection and the multi-branch structure with channel attention to improve the feature extraction capability; it also applies a task-decoupled multi-detector structure to improve detection accuracy; focus loss functions are used as the loss functions of classification and confidence

and the complete intersection over union loss function is used as the loss function of prediction box regression in the model training. The proposed algorithm achieves an average precision of 92.7% and F1-score of 87.4% on the real fuze burst point image dataset. The results show the superiority of the proposed algorithm over the existing typical models in the fuze burst point detection task.

关键词

Keywords

references

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