SViT: A Novel Multimodal Learning Approach for Ship Distance Estimation via Time-Series Data Visualization 


Vol. 14,  No. 3, pp. 203-213, Mar.  2025
https://doi.org/10.3745/TKIPS.2025.14.3.203


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  Abstract

Accurately detecting and estimating ship distances is crucial for maritime safety, resource management, and preventing illegal activities. However, traditional Automatic Identification Systems (AIS) are highly vulnerable to signal interference and tampering, making them unreliable. To address this issue, we propose SViT, a novel multimodal learning approach. SViT converts time-series sensor data into 2D image representations, allowing Vision Transformer (ViT) models to analyze them effectively. It further enhances computational efficiency and noise reduction through hierarchical sensor feature selection. To ensure stable ship movement predictions, we introduce a novel loss function combining MSE, Smoothness Loss, and Gradient Loss. Experimental results show that SViT outperforms LSTM-based models in training speed and predictive stability. This study presents a robust framework for utilizing complex multisensor data, with potential applications in security, surveillance, and maintenance.

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[IEEE Style]

S. Choi, J. Choi, H. Chang, J. An, "SViT: A Novel Multimodal Learning Approach for Ship Distance Estimation via Time-Series Data Visualization," The Transactions of the Korea Information Processing Society, vol. 14, no. 3, pp. 203-213, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.203.

[ACM Style]

Sun Choi, Jeongmin Choi, Hyunbae Chang, and Jhonghyun An. 2025. SViT: A Novel Multimodal Learning Approach for Ship Distance Estimation via Time-Series Data Visualization. The Transactions of the Korea Information Processing Society, 14, 3, (2025), 203-213. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.203.

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