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Postdoctoral Science Foundation","award":["51909039"],"award-info":[{"award-number":["51909039"]}]},{"name":"Chinese Postdoctoral Science Foundation","award":["2022ZLGX04"],"award-info":[{"award-number":["2022ZLGX04"]}]},{"name":"Chinese Postdoctoral Science Foundation","award":["2021ZLGX05"],"award-info":[{"award-number":["2021ZLGX05"]}]},{"name":"Chinese Postdoctoral Science Foundation","award":["ZR2020MF017"],"award-info":[{"award-number":["ZR2020MF017"]}]},{"name":"Chinese Postdoctoral Science Foundation","award":["2020M672123"],"award-info":[{"award-number":["2020M672123"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection based on remote sensing images holds significant importance in both military and economic domains. Ships within such images exhibit diverse scales, dense distributions, arbitrary orientations, and narrow shapes, which pose challenges for accurate recognition. This paper introduces an improved S2A-Net (Single-shot Alignment Network) based oriented object detection algorithm for ship detection. In network structure, pyramid squeeze attention is embedded in order to focus on key features and a context information module is designed to enhance the context understanding capability of the network. In the training strategy, considering the distortion problems such as blurring and low contrast in remote sensing images, a fog density and depth decomposition-based unpaired image dehazing network D4 is adopted to improve the image quality, besides, an image weight sampling strategy is proposed to enhance the training opportunities of small and difficult samples, thereby mitigating the issue of imbalanced ship category distribution. Experimental results demonstrate that the improved S2A-Net algorithm achieves the mean average precision of 77.27% for ship detection in the FAIR1M dataset, which is 5.6% better than the original S2A-Net algorithm, and outperforms the current common object detection algorithms.<\/jats:p>","DOI":"10.3390\/rs15184559","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:32:27Z","timestamp":1694993547000},"page":"4559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["An Improved S2A-Net Algorithm for Ship Object Detection in Optical Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6893-9028","authenticated-orcid":false,"given":"Jianfeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"},{"name":"Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems, Ministry of Industry and Information Technology, Weihai 264209, China"},{"name":"Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-8848","authenticated-orcid":false,"given":"Mingxu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"},{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0168-1245","authenticated-orcid":false,"given":"Qinghua","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"},{"name":"Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems, Ministry of Industry and Information Technology, Weihai 264209, China"},{"name":"Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China"},{"name":"Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems, Ministry of Industry and Information Technology, Weihai 264209, China"},{"name":"Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"916","DOI":"10.37188\/OPE.2020.0419","article-title":"Research Progress on Vessel Detection Using Optical Remote Sensing Image","volume":"29","author":"Xu","year":"2021","journal-title":"Opt. 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