Newly Equipped Armored Vehicle Classification Based on Integrated Transfer Learning

doi:10.12382/bgxb.2022.0412

Abstract

Abstract:

In complicated land warfare environments, image classification techniques is an important tool to quickly distinguish armored vehicle targets. To address the problem that the existing mainstream classification algorithms based on Convolutional Neural Network (CNN) have high requirements for the number and quality of training samples and perform with insufficient accuracy in the image classification task of newly equipped armored vehicles, a transfer learning method that integrates two CNNs based on different learning strategies is proposed. Specifically, one CNN is pre-trained on an old-fashioned armored vehicle image dataset whose samples can be easily obtained and have sufficient quantity to learn local detail features. The other CNN is pre-trained on the dataset of virtual images of the newly equipped armored vehicles with a low image quality to learn the global features. The pre-trained CNNs are all fine-tuned according to different strategies using a limited number of real samples of newly equipped armored vehicles to improve the characterization capability. A self-learning model integration mechanism based on the Optuna hyperparametric optimization framework is designed, which can autonomously weight the outputs of the two CNNs for optimization and further improve the classification accuracy of the algorithm. The experimental results show that the accuracy of the proposed algorithm is improved by 7% in the image classification task of newly equipped armored vehicles compared with the same model trained from scratch, which effectively alleviates the problem of insufficient training samples.

Key words: transfer learning, convolutional neural network, armored vehicles, feature extraction, model integration mechanism

CLC Number:

TP181

LIU Yi, REN Jihuan, WU Xiang, BO Yuming. Newly Equipped Armored Vehicle Classification Based on Integrated Transfer Learning[J]. Acta Armamentarii, 2023, 44(8): 2319-2328.

Figures/Tables 8

Fig.1 Flow chart of the proposed method

Fig.2 Schematic diagram of the ResNet-18 structure

Table 1 Network structure of the ResNet-18

模块名	输出尺寸	构成
卷积模块1	56×56	7×7×64,3×3 池化
残差模块1	56×56	3×3×64、3×3×64堆叠2次
残差模块2	28×28	3×3×128、3×3×128堆叠2次
残差模块3	14×14	3×3×256、3×3×256堆叠2次
残差模块4	7×7	3×3×512、3×3×512堆叠2次
输出层	1×1	均值池化, 全连接层, SoftMax激活函数

Fig.3 Schematic diagram of category composition of datasets

Table 2 Introduction to the datasets

数据集	总样本数	训练样本数	测试样本数	每类样本数
D₁	1000	800	200	200
D₂	500	300	200	100
$D ¯ 2$	250	40	10	50

Table 2 Introduction to the datasets

数据集	总样本数	训练样本数	测试样本数	每类样本数
D₁	1000	800	200	200
D₂	500	300	200	100
$D ¯ 2$	250	40	10	50

Table 3 Experimental results

分类方法	测试数据集正确率
分类方法	30张D₂学习样本	60张D₂学习样本
HOG+SVM	54	62
深度学习	60	74
迁移学习 (D₁)	63	74
迁移学习 ( $D ¯ 2$ )	64	74
本文方法	67	76.5

Table 3 Experimental results

分类方法	测试数据集正确率
分类方法	30张D₂学习样本	60张D₂学习样本
HOG+SVM	54	62
深度学习	60	74
迁移学习 (D₁)	63	74
迁移学习 ( $D ¯ 2$ )	64	74
本文方法	67	76.5

Fig.4 Schematic diagram of classification accuracy and standard deviation of accuracy for the two methods

Table 4 Attention results of global structure extractor and local feature extractor

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