A Method for Military Aircraft Recognition Using a Coordinate Attention-based Deep Learning Network

doi:10.12382/bgxb.2023.0526

Abstract

Abstract:

The military combat information can be provided by using the visible images to effectively distinguish the types of enemy aircrafts used for military operations in a rapidly changing battlefield environment. To address the challenges associated with extracting the model features from small aircraft targets and complex environmental backgrounds, as well as the limited training data available in existing military aircraft recognition methods, this paper proposes a ConvNeXt with coordinate attention(ConvNeXt-CA)-based recognition method for military aircraft targets. Based on the fact that the ConvNeXt-CA network can retain the characteristics of small aircrafttargets, the proposed method introduces the CA mechanism to design a CA-Stage module, which improves the ability of the network to distinguish between background and foreground.It uses data augmentation to expand the data set and the migration learning strategy to improve the generalization capability of the model, and trains the ConvNeXt-CA network with optimal hyperparameters.The experimental results show that, compared with the traditional military aircraft identification methods and other deep learning models, the migration learning-based ConvNeXt-CA network has a significantly improved prediction accuracy and a strong generalization capability.

Key words: military aircraft recognition, deep convolutional neural network, coordinate attention mechanism, migration learning

CLC Number:

TJ85

YANG Huanyu, WANG Jun, WU Xiang, BO Yuming, MA Lifeng, LU Jinlei. A Method for Military Aircraft Recognition Using a Coordinate Attention-based Deep Learning Network[J]. Acta Armamentarii, 2024, 45(7): 2128-2143.

Figures/Tables 21

Fig.1 Structure diagram of ConvNeXt network

Fig.2 Structure diagram of the improved ConvNeXt-CA network

Fig.3 Schematic diagram of coordinate attention module

Fig.4 Flow chart of ConvNeXt-CA network using migration learning strategy

Fig.5 Overall flow chart of model training and prediction

Fig.6 Images of different types of military aircraft

Fig.7 Obscured images of military aircrafts

Fig.8 Images of small target military aircrafts

Fig.9 Additional images of military aircrafts

Table 1 Best accuracies of different models

算法模型	预测准确度/%	epoch
HOG+SVM	68.6	1
ResNet50	90.4	20
ResNet50-CBAM	89.8	20
ResNet50-CA	91.5	20
Swin-T	92.0	20
VAN-B2	94.3	20
ConvNeXt	93.5	20
ConvNeXt-CBAM	94.7	20
ConvNeXt-CA-a	94.8	20
ConvNeXt-CA	96.0	20

Fig.10 Loss value and accuracy curves of different models

Table 2 Confusion matrix underlying index relation

混淆矩阵		真实值
混淆矩阵		正样本	负样本
预测值	正样本	TP	FP
	负样本	FN	TN

Table 3 The evaluation index values of different models

算法模型	精确率/%	召回率/%	特异度/%
ResNet50	91.04	90.13	99.70
ResNet50-CBAM	90.97	89.19	99.69
ResNet50-CA	91.87	91.01	99.73
Swin-T	92.76	91.31	99.76
ConvNeXt	93.71	92.75	99.80
ConvNeXt-CBAM	95.73	93.86	99.83
ConvNeXt-CA-a	95.15	94.34	99.84
ConvNeXt-CA	96.12	95.33	99.88

Fig.11 ConvNeXt confusion matrix

Fig.12 ConvNeXt-CA confusion matrix

Fig.13 Heat maps of different models(left: the attention heatmaps of different models when recognizing F35 aircraft, middle: C17, and right: EF2000)

Table 4 Recognition accuraciesandrun times of different methods

算法模型	准确度/%	运行时间/s
ConvNeXt	93.5	4.60
ConvNeXt+CA	96	4.62

Table 5 The accuracies of different methods

算法模型	训练准确度/%	epoch
ResNet50+迁移学习	86.1	20
ResNet50随机初始化	70.4	20
ConvNeXt+迁移学习	94.4	20
ConvNeXt随机初始化	83.1	20

Fig.14 Schematic diagram of training recognition accuracies of different methods

Table 6 Effects of different improved methods on military aircraft image recognition results

算法模型	迁移学习	CA	Epoch	预测准确度/%
			20	82.6
ConvNeXt	P		20	93.5
		P	20	84.2
	P	P	20	96.0

Fig.15 Display of partial recognition results

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