An algorithm of ship image classification based on improved ResNet

Zengrui Xu

doi:10.1117/12.3038725

23 August 2024 An algorithm of ship image classification based on improved ResNet

Zengrui Xu

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132500C (2024) https://doi.org/10.1117/12.3038725
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Vessels as the main carrier of ocean transportation, when the ship is completed with intelligent transformation, the target detection and classification recognition problems of such ships will have an important impact on maritime traffic and national defense security. However, to enhance target detection performance, it often requires the use of larger and deeper neural networks, which in turn consume a substantial amount of computational resources and thus energy. This is particularly troubling for non-nuclear-powered ships that have limited energy resources. To address this issue, this paper presents an improved ship image classification method, the Dynamic Convolutional Network and Triplet Attention Mechanism Integrated ResNet (DY-ResNet-TA). Through theoretical analysis and experiments based on a ship image dataset, the proposed method has been proven to significantly improve model performance with a minimal increase in computational resource consumption.

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

Citation Download Citation

Zengrui Xu "An algorithm of ship image classification based on improved ResNet", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132500C (23 August 2024); https://doi.org/10.1117/12.3038725

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KEYWORDS

Convolution

Image classification

Neural networks

Feature extraction

Data modeling

Target detection

Coastal modeling

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