{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T03:55:48Z","timestamp":1776138948337,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61975043"],"award-info":[{"award-number":["61975043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0502902"],"award-info":[{"award-number":["2017YFB0502902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.<\/jats:p>","DOI":"10.3390\/rs13163059","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:47:52Z","timestamp":1628066872000},"page":"3059","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4418-605X","authenticated-orcid":false,"given":"Jianming","family":"Hu","sequence":"first","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-8480","authenticated-orcid":false,"given":"Xiyang","family":"Zhi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Tianjun","family":"Shi","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Yang","family":"Cui","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites for Chinese Academy of Sciences, Shanghai 201220, China"}]},{"given":"Shenggang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites for Chinese Academy of Sciences, Shanghai 201220, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7147","DOI":"10.1109\/TGRS.2018.2848901","article-title":"HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. 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