{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:21:03Z","timestamp":1779366063495,"version":"3.53.0"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Pujiang Program","award":["22PJD028"],"award-info":[{"award-number":["22PJD028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect ratios, and high computational demands. In this paper, we propose an improved target detection model, named YOLOv5-ASC, to address the challenges in maritime target detection. The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). These components work together to enhance the model\u2019s performance in detecting complex maritime targets by improving its ability to capture multi-scale features, optimize the localization process, and adapt to the large aspect ratios typical of maritime objects. Experimental results show that, compared to the original YOLOv5 model, YOLOv5-ASC achieves a 4.36 percentage point increase in mAP@0.5 and a 9.87 percentage point improvement in precision, while maintaining computational complexity within a reasonable range. The proposed method not only achieves significant performance improvements on the ShipRSImageNet dataset but also demonstrates strong potential for application in complex maritime remote sensing scenarios.<\/jats:p>","DOI":"10.3390\/info16040332","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T20:38:26Z","timestamp":1745267906000},"page":"332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimized Marine Target Detection in Remote Sensing Images with Attention Mechanism and Multi-Scale Feature Fusion"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2226-482X","authenticated-orcid":false,"given":"Xiantao","family":"Jiang","sequence":"first","affiliation":[{"name":"Department Information Engineering, Shanghai Maritime University, Shanghai 200135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0877-1598","authenticated-orcid":false,"given":"Tianyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department Information Engineering, Shanghai Maritime University, Shanghai 200135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4017-0074","authenticated-orcid":false,"given":"Tian","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Tokushima University, Tokushima 770-0855, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Cen","sequence":"additional","affiliation":[{"name":"Ningbo Special Equipment Inspection and Research Institute, Ningbo 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, J., Wang, X., Wu, L., Feng, K., and Wang, G. 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