{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:31:12Z","timestamp":1775176272370,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"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":["U1904120"],"award-info":[{"award-number":["U1904120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["182102310759"],"award-info":[{"award-number":["182102310759"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018RCJH09"],"award-info":[{"award-number":["2018RCJH09"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan Science and Technology Department Science and Technology Research Program","award":["U1904120"],"award-info":[{"award-number":["U1904120"]}]},{"name":"Henan Science and Technology Department Science and Technology Research Program","award":["182102310759"],"award-info":[{"award-number":["182102310759"]}]},{"name":"Henan Science and Technology Department Science and Technology Research Program","award":["2018RCJH09"],"award-info":[{"award-number":["2018RCJH09"]}]},{"name":"Fundamental Research Funds for the Henan Provincial Colleges and Universities","award":["U1904120"],"award-info":[{"award-number":["U1904120"]}]},{"name":"Fundamental Research Funds for the Henan Provincial Colleges and Universities","award":["182102310759"],"award-info":[{"award-number":["182102310759"]}]},{"name":"Fundamental Research Funds for the Henan Provincial Colleges and Universities","award":["2018RCJH09"],"award-info":[{"award-number":["2018RCJH09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic ship detection in SAR images plays an essential role in both military and civilian fields. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To solve this problem, an efficient lightweight network YOLO-Lite is proposed for SAR ship detection in this paper. First, a lightweight feature enhancement backbone (LFEBNet) is designed to reduce the amount of calculation. Additionally, a channel and position enhancement attention (CPEA) module is constructed and embedded into the backbone network to more accurately locate the target location by capturing the positional information. Second, an enhanced spatial pyramid pooling (EnSPP) module is customized to enhance the expression ability of features and address the position information loss of small SAR ships in high-level features. Third, we construct an effective multi-scale feature fusion network (MFFNet) with two feature fusion channels to obtain feature maps with more position and semantic information. Furthermore, a novel confidence loss function is proposed to effectively improve the SAR ship target detection accuracy. Extensive experiments on SSDD and SAR ship datasets verify the effectiveness of our YOLO-Lite, which can not only accurately detect SAR ships in different backgrounds but can also realize a lightweight architecture with low computation cost.<\/jats:p>","DOI":"10.3390\/rs15153771","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3771","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaozhen","family":"Ren","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"},{"name":"Key Laboratory of Grain Information Processing & Control, College of Information Science and Engineering, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China"}]},{"given":"Yanwen","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"},{"name":"Key Laboratory of Grain Information Processing & Control, College of Information Science and Engineering, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5215212","DOI":"10.1109\/TGRS.2021.3113919","article-title":"Spatial singularity-exponent-domain multiresolution imaging-based SAR ship target detection method","volume":"60","author":"Xiong","year":"2022","journal-title":"IEEE Trans. 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