ArticleJournal of imaging2024
Enhanced Atrous Spatial Pyramid Pooling Feature Fusion for Small Ship Instance Segmentation.
Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- PHSNet: A Small-Target Infrared Hotspot Detection Network for Photovoltaic Modules in UAV Remote-Sensing Images.Journal of imaging · 2026Article
- Development of a deep learning-based foreign object detection algorithm for coal mine conveyor belts.Scientific reports · 2025Article
- Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules.Scientific reports · 2025Article
- Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in inaccurate ship segmentation. To address this, we propose a novel solution called enhanced Atrous Spatial Pyramid Pooling (ASPP) feature fusion for small ship instance segmentation. The enhanced ASPP feature fusion module focuses on small objects by refining them and fusing important features. The framework consistently outperforms state-of-the-art models, including Mask R-CNN, Cascade Mask R-CNN, YOLACT, SOLO, and SOLOv2, in three diverse datasets, achieving an average precision (mask AP) score of 75.8% for ShipSG, 69.5% for ShipInsSeg, and 54.5% for the MariBoats datasets.
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