{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:43:15Z","timestamp":1783726995840,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The real-time performance of ship detection is an important index in the marine remote sensing detection task. Due to the computing resources on the satellite being limited by the solar array size and the radiation-resistant electronic components, information extraction tasks are usually implemented after the image is transmitted to the ground. However, in recent years, the one-stage based target detector such as the You Only Look Once Version 5 (YOLOv5) deep learning framework shows powerful performance while being lightweight, and it provides an implementation scheme for on-orbit reasoning to shorten the time delay of ship detention. Optimizing the lightweight model has important research significance for SAR image onboard processing. In this paper, we studied the fusion problem of two lightweight models which are the Coordinate Attention (CA) mechanism module and the YOLOv5 detector. We propose a novel lightweight end-to-end object detection framework fused with a CA module in the backbone of a suitable position: YOLO Coordinate Attention SAR Ship (YOLO-CASS), for the SAR ship target detection task. The experimental results on the SSDD synthetic aperture radar (SAR) remote sensing imagery indicate that our method shows significant gains in both efficiency and performance, and it has the potential to be developed into onboard processing in the SAR satellite platform. The techniques we explored provide a solution to improve the performance of the lightweight deep learning-based object detection framework.<\/jats:p>","DOI":"10.3390\/s22093370","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"3370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9354-6093","authenticated-orcid":false,"given":"Fang","family":"Xie","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"Shanghai Engineering Center for Microsatellites, Shanghai 201304, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojun","family":"Lin","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"Shanghai Engineering Center for Microsatellites, Shanghai 201304, China"},{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"Shanghai Engineering Center for Microsatellites, Shanghai 201304, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1109\/JSTARS.2016.2551730","article-title":"A Comparative Study of Operational Vessel Detectors for Maritime Surveillance Using Satellite-Borne Synthetic Aperture Radar","volume":"9","author":"Stasolla","year":"2019","journal-title":"IEEE J. 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