FastPFM: a multi-scale ship detection algorithm for complex scenes based on SAR images-Reference-Cited by-同舟云学术

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FastPFM: a multi-scale ship detection algorithm for complex scenes based on SAR images

Published:2024-02-19 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Wang Wei¹,Han Dezhi¹,Chen Chongqing¹,Wu Zhongdai²

Affiliation:

1. School of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China

2. Waterway Traffic Control National Key Laboratory, State Key Laboratory of Maritime Technology and Safety, Shanghai, People’s Republic of China

Funder

National Key Research

Natural Science Foundation of Shanghai

Shanghai Maritime University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2024.2313854

Reference65 articles.

1. Cascade R-CNN: High Quality Object Detection and Instance Segmentation

2. MPCCT: Multimodal vision-language learning paradigm with context-based compact Transformer

3. CAAN: Context-Aware attention network for visual question answering

4. CLVIN: Complete language-vision interaction network for visual question answering

5. Chen J. Kao S.-h. He H. Zhuo W. Wen S. Lee C.-H. & Chan S.-H. G. (2023). Run don't walk: Chasing higher FLOPS for faster neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. YOLOv8-FDF: A Small Target Detection Algorithm in Complex Scenes;IEEE Access;2024

2. Scale-Mixing Enhancement and Dual Consistency Guidance for End-to-End Semisupervised Ship Detection in SAR Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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