A Fuze Burst Point Detection Method for Outfield Test Images

doi:10.12382/bgxb.2022.0300

Abstract

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.

Key words: burst point observation, target detection, deep learning, convolutional neural network, feature

CLC Number:

TP391

ZHOU Yu, CAO Ronggang, LI Ping, MA Xiao. A Fuze Burst Point Detection Method for Outfield Test Images[J]. Acta Armamentarii, 2023, 44(8): 2453-2464.

Figures/Tables 16

Fig.1 Burst point shape under different conditions

Fig.2 Schematic diagram of overall layout relationship of observation equipment

Fig.3 Structure of ConvNeXt network

Fig.4 The original multi-branch structure and the multi-branch with channel attention module

Fig.5 Original CSP Module and proposed DCSP Module

Fig.6 Decoupling detection head

Fig.7 The overall structure of the proposed detection algorithm model

Table 1 Information statistics of the annotated ground truth box of dataset target

目标类别	目标总数	面积占比/%
目标类别	目标总数	最大值	最小值	中位数	均值
1	2578	11.04	0.10	1.91	2.18
2	2180	6.94	1.27	3.19	3.59
3	2020	10.87	2.14	5.19	5.39

Fig.8 Schematic diagram of the data augmentation

Table 2 Effects of improved designs of backbone and head on detection model performance

实验组	骨干网络	检测头	准确率/%	召回率/%	平均精度/%	F1分数/%
模型1	DarkNet53	多检测头	74.6	55.0	74.7	59.8
模型2	ConvNeXt	单检测头	87.8	56.5	76.9	55.6
模型3	ConvNeXt	多检测头	85.0	57.2	79.8	65.7

Fig.9 The PR curves of detection models in the ablation experiment of improved designs of backbone and head

Table 3 Effects of improvement measures of neck on detection model performance

实验组	DCSP 模块	MBCA 模块	准确率/%	召回率/ %	平均精度/%	F1分数/%
模型3			85.0	57.2	79.8	65.7
模型4	√		84.2	85.1	88.6	84.5
模型5		√	89.3	84.6	90.7	86.8
模型6	√	√	87.3	87.8	92.7	87.4

Fig.10 The PR curves of detection models in the ablation experiment of improvement measures for neck structure

Table 4 Comparison of the detection results before and after the improvement of the neck structure of the detection model

Fig.11 Enhancement effect of improved neck structure on feature map

Table 5 Performance comparison of different detection models based on the proposed dataset

检测模型	准确率/%	召回率/%	平均精度/%	F1分数/%
Faster RCNN 模型	63.2	85.4	81.3	70.2
SSD模型	78.4	62.3	74.5	61.4
YOLOv3模型	80.5	59.4	76.1	64.8
YOLOX模型	87.9	74.1	87.5	81.1
本文模型	87.3	87.8	92.7	87.4

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