{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:10:16Z","timestamp":1775819416533,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CASEarth Minisatellite Thermal Infrared Spectrometer Project","award":["XDA19010102"],"award-info":[{"award-number":["XDA19010102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61975222"],"award-info":[{"award-number":["61975222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The automatic ship detection method for thermal infrared remote sensing images (TIRSIs) is of great significance due to its broad applicability in maritime security, port management, and target searching, especially at night. Most ship detection algorithms utilize manual features to detect visible image blocks which are accurately cut, and they are limited by illumination, clouds, and atmospheric strong waves in practical applications. In this paper, a complete YOLO-based ship detection method (CYSDM) for TIRSIs under complex backgrounds is proposed. In addition, thermal infrared ship datasets were made using the SDGSAT-1 thermal imaging system. First, in order to avoid the loss of texture characteristics during large-scale deep convolution, the TIRSIs with the resolution of 30 m were up-sampled to 10 m via bicubic interpolation method. Then, complete ships with similar characteristics were selected and marked in the middle of the river, the bay, and the sea. To enrich the datasets, the gray value stretching module was also added. Finally, the improved YOLOv5 s model was used to detect the ship candidate area quickly. To reduce intra-class variation, the 4.23\u20137.53 aspect ratios of ships were manually selected during labeling, and 8\u201310.5 \u03bcm ship datasets were constructed. Test results show that the precision of the CYSDM is 98.68%, which is 9.07% higher than that of the YOLOv5s algorithm. CYSDM provides an effective reference for large-scale, all-day ship detection.<\/jats:p>","DOI":"10.3390\/rs14071534","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T23:30:23Z","timestamp":1647991823000},"page":"1534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["A Complete YOLO-Based Ship Detection Method for Thermal Infrared Remote Sensing Images under Complex Backgrounds"],"prefix":"10.3390","volume":"14","author":[{"given":"Liyuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-9224","authenticated-orcid":false,"given":"Linyi","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jingwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Siqi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2244-8327","authenticated-orcid":false,"given":"Fansheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China"},{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 10094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-like algorithm for SAR images","volume":"53","author":"Dellinger","year":"2014","journal-title":"IEEE Trans. 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