Research on automatic classification of image resources based on deep learning algorithms

Lin Li

doi:10.1117/12.3052734

19 November 2024 Research on automatic classification of image resources based on deep learning algorithms

Lin Li

Proceedings Volume 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024); 133970O (2024) https://doi.org/10.1117/12.3052734
Event: 4th International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 2024, Guiyang, China

Abstract

This paper explores how to enhance the performance of deep learning image classification models by introducing attention mechanisms and optimizing feature extraction methods. Techniques such as CBAM (Convolutional Block Attention Module) and depthwise separable convolutions were employed, with experiments showing that the CBAM model achieved an accuracy of 89.2%, while the optimized model reduced computational cost to 1.20 GFLOPs. These results validate the effectiveness of the proposed methods in improving classification accuracy and reducing computational costs, offering new directions for future research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Li "Research on automatic classification of image resources based on deep learning algorithms", Proc. SPIE 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 133970O (19 November 2024); https://doi.org/10.1117/12.3052734

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