Study on the application of transfer learning in small sample image classification of military equipment

Leiji Lu; Hongxia Yu; Hongju Xiao; Lei Bao

doi:10.1117/12.3053163

3 January 2025 Study on the application of transfer learning in small sample image classification of military equipment

Leiji Lu, Hongxia Yu, Hongju Xiao, Lei Bao

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344207 (2025) https://doi.org/10.1117/12.3053163
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

To address the issue of insufficient military equipment sample data, which cannot meet the training requirements of deep neural networks and tends to cause overfitting, this paper introduces transfer learning technology to solve the small-sample classification problem for military equipment. By constructing a multi-type sample training set and fine-tuning the convolutional layers of pre-trained models, specific target classifiers are trained. Practice has proven that the application of transfer learning in small-sample classification tasks saves model training time, resolves issues related to model overfitting and strong dependence on data labels, and effectively improves the accuracy of image classification based on deep learning for small samples of military equipment.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Leiji Lu, Hongxia Yu, Hongju Xiao, and Lei Bao "Study on the application of transfer learning in small sample image classification of military equipment", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344207 (3 January 2025); https://doi.org/10.1117/12.3053163

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KEYWORDS

Education and training

Deep learning

Image classification

Feature extraction

Statistical modeling

Instrument modeling

Neural networks

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