CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic...
CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection
About this item
Full title
Author / Creator
Zhang, Lei , Zheng, Jiachun , Li, Chaopeng , Xu, Zhiping , Yang, Jiawen , Wei, Qiuxin and Wu, Xinyi
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
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Scope and Contents
Contents
The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarel...
Alternative Titles
Full title
CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c47dce7658a24da09f3a1849096936ea
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c47dce7658a24da09f3a1849096936ea
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s24061793
