Article
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Multi-Teacher D-S Fusion for Semi-Supervised SAR Ship Detection
Version 1
: Received: 18 June 2024 / Approved: 19 June 2024 / Online: 19 June 2024 (07:18:43 CEST)
How to cite: Zhang, X.; Li, J.; Li, C.; Liu, G. Multi-Teacher D-S Fusion for Semi-Supervised SAR Ship Detection. Preprints 2024, 2024061318. https://doi.org/10.20944/preprints202406.1318.v1 Zhang, X.; Li, J.; Li, C.; Liu, G. Multi-Teacher D-S Fusion for Semi-Supervised SAR Ship Detection. Preprints 2024, 2024061318. https://doi.org/10.20944/preprints202406.1318.v1
Abstract
Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.
Keywords
synthetic aperture radar (SAR); ship detection; deep learning; semi-supervised learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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