Convolutional Neural Network Transfer Learning Method for Aircraft Image Classification
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Дата
2025
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This study presents a transfer learning method for aircraft image classification using convolutional neural networks (CNNs), with ResNet-50 selected as the foundational architecture. In light of limited annotated datasets and diverse imaging conditions across aerial platforms, the approach leverages pre-trained models to extract generic visual features, followed by fine-tuning on domain-specific aircraft imagery. The proposed method includes preprocessing techniques, selective layer freezing, and architectural adaptation to enhance classification accuracy while maintaining computational efficiency. Experiments demonstrate improved performance over models trained from scratch, particularly in terms of convergence speed, generalisation under occlusion and variable lighting, and adaptability to various aircraft types. The solution is optimised for real-time deployment, including on resource-constrained devices. This work highlights the practical value of CNN-based transfer learning frameworks in aviation surveillance, airport automation, and security monitoring, and suggests avenues for future research involving lightweight architectures and video-based classification.
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Bas I.S., Kadynska V.D., Klimenko V.I., Mazurets O.V. Convolutional Neural Network Transfer Learning Method for Aircraft Image Classification. Scientific method: reality and future trends of researching. Proceedings of VI International Scientific and Theoretical Conference. June 6, 2025. Montreal, Canada. Pp. 147-155