Journal Article

Fine-grained ship image classification and detection based on a vision transformer and multi-grain feature vector FPN model

Chen, Yongbing, Yu, Deying, Huang, Liang, Wang, Zhiguo, Wang, Fengxiang, Zhang, Yalun

2025

Overview

In naval and civilian domains, meticulous ship classification and detection are paramount. Nevertheless, predominant research has gravitated toward leveraging Convolutional Neural Network (CNN)-centered methodologies, often overlooking the diverse granularity inherent in ship samples. In our pursuit to holistically extract features from ship images across varying granularities, we present a transformative architecture: the Vision Transformer and Multi-Grain Feature Vector Feature Pyramid Network (ViT-MGFV-FPN). This model synergistically melds the merits of MGFV-FPN with an augmented Vision Transformer (ViT) for a comprehensive image feature extraction. To cater to the extraction of broader image features whilst sidestepping the innate quadratic complexity of traditional ViT, we unveil an enhanced version christened the Global Swin Transformer. Concurrently, the MGFV-FPN is orchestrated to harness the prowess of CNNs in distilling intricate ship attributes. Rigorous empirical evaluations underscore our model's superiority in juxtaposition with extant CNN and transformer-based paradigms for nuanced ship categorization.

• Publisher: Taylor & Francis,Taylor & Francis Ltd,Combat Command Department,People's Liberation Army%Department of Operational Research and Planning,Na%State Key Laboratory of High Performance Computing%School of Electrical Engineering,Naval University %College of Electronic Engineering,Naval University,Taylor & Francis Group
• Subject: Algorithms / Architecture / Artificial neural networks / Classification / Datasets / Deep learning / Engineering / Feature extraction / Image classification / Neural networks / remote-sensing images / ship detection / transformer / Vision transformers