{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T01:55:04Z","timestamp":1782784504193,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2021 project of Guangdong Province Science and Technology Special Funds (\u201cCollege Special Project + Task List\u201d) Competitive Distribution","award":["2021A05237"],"award-info":[{"award-number":["2021A05237"]}]},{"name":"2021 project of Guangdong Province Science and Technology Special Funds (\u201cCollege Special Project + Task List\u201d) Competitive Distribution","award":["230420023"],"award-info":[{"award-number":["230420023"]}]},{"name":"2021 project of Guangdong Province Science and Technology Special Funds (\u201cCollege Special Project + Task List\u201d) Competitive Distribution","award":["080507112201"],"award-info":[{"award-number":["080507112201"]}]},{"name":"2021 project of Guangdong Province Science and Technology Special Funds (\u201cCollege Special Project + Task List\u201d) Competitive Distribution","award":["R20065"],"award-info":[{"award-number":["R20065"]}]},{"name":"project of Enhancing School with Innovation of Guangdong Ocean University","award":["2021A05237"],"award-info":[{"award-number":["2021A05237"]}]},{"name":"project of Enhancing School with Innovation of Guangdong Ocean University","award":["230420023"],"award-info":[{"award-number":["230420023"]}]},{"name":"project of Enhancing School with Innovation of Guangdong Ocean University","award":["080507112201"],"award-info":[{"award-number":["080507112201"]}]},{"name":"project of Enhancing School with Innovation of Guangdong Ocean University","award":["R20065"],"award-info":[{"award-number":["R20065"]}]},{"name":"program for scientific research start-up funds of Guangdong Ocean University","award":["2021A05237"],"award-info":[{"award-number":["2021A05237"]}]},{"name":"program for scientific research start-up funds of Guangdong Ocean University","award":["230420023"],"award-info":[{"award-number":["230420023"]}]},{"name":"program for scientific research start-up funds of Guangdong Ocean University","award":["080507112201"],"award-info":[{"award-number":["080507112201"]}]},{"name":"program for scientific research start-up funds of Guangdong Ocean University","award":["R20065"],"award-info":[{"award-number":["R20065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning techniques have been extensively used to detect ships in synthetic aperture radar (SAR) images. The majority of modern algorithms can achieve successful ship detection outcomes when working with multiple-scale ships on a large sea surface. However, there are still issues, such as missed detection and incorrect identification when performing multi-scale ship object detection operations in SAR images of complex scenes. To solve these problems, this paper proposes a complex scenes multi-scale ship detection model, according to YOLOv7, called CSD-YOLO. First, this paper suggests an SAS-FPN module that combines atrous spatial pyramid pooling and shuffle attention, allowing the model to focus on important information and ignore irrelevant information, reduce the feature loss of small ships, and simultaneously fuse the feature maps of ship targets on various SAR image scales, thereby improving detection accuracy and the model\u2019s capacity to detect objects at several scales. The model\u2019s optimization is then improved with the aid of the SIoU loss function. Finally, thorough tests on the HRSID and SSDD datasets are presented to support our methodology. CSD-YOLO achieves better detection performance than the baseline YOLOv7, with a 98.01% detection accuracy, a 96.18% recall, and a mean average precision (mAP) of 98.60% on SSDD. In addition, in comparative experiments with other deep learning-based methods, in terms of overall performance, CSD-YOLO still performs better.<\/jats:p>","DOI":"10.3390\/rs15082071","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T04:54:24Z","timestamp":1681448064000},"page":"2071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Multi-Scale Ship Detection Algorithm Based on YOLOv7 for Complex Scene SAR Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhuo","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Robotic Laboratory, Institute of Automatics and Control Process of Russian Academy of Sciences, 5, Radio St., 690041 Vladivostok, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Filaretov","sequence":"additional","affiliation":[{"name":"Robotic Laboratory, Institute of Automatics and Control Process of Russian Academy of Sciences, 5, Radio St., 690041 Vladivostok, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Yukhimets","sequence":"additional","affiliation":[{"name":"Robotic Laboratory, Institute of Automatics and Control Process of Russian Academy of Sciences, 5, Radio St., 690041 Vladivostok, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Hao, Y. 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