{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T02:38:00Z","timestamp":1769481480431,"version":"3.49.0"},"reference-count":84,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Innovation Team Project of the Liaoning Provincial Department of Education","award":["LJ222510144004"],"award-info":[{"award-number":["LJ222510144004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Oriented object detection constitutes a fundamental yet challenging task in Artificial Intelligence of Things (AIoT)-enabled maritime surveillance, where real-time processing of dense visual streams is imperative. However, existing detectors suffer from three critical limitations: sequential attention mechanisms that fail to capture coupled spatial\u2013channel dependencies, unconstrained deformable convolutions that yield unstable predictions for elongated vessels, and center-based distance metrics that ignore angular alignment in sample assignment. To address these challenges, we propose JAOSD (Joint Attention-based Oriented Ship Detection), an anchor-free framework incorporating three novel components: (1) a joint attention module that processes spatial and channel branches in parallel with coupled fusion, (2) an adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization, and (3) an orientation-aware Adaptive Sample Selection strategy based on corner-aware distance metrics. Extensive experiments on three benchmarks demonstrate that JAOSD achieves state-of-the-art performance\u201494.74% mAP on HRSC2016, 92.43% AP50 on FGSD2021, and 80.44% mAP on DOTA v1.0\u2014while maintaining real-time inference at 42.6 FPS. Cross-domain evaluation on the Singapore Maritime Dataset further confirms robust generalization capability from aerial to shore-based surveillance scenarios without domain adaptation.<\/jats:p>","DOI":"10.3390\/s26030767","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T16:13:48Z","timestamp":1769444028000},"page":"767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Ship Detection Algorithm Based on Visual Sensor Signal Processing for AIoT-Enabled Maritime Surveillance Automation"],"prefix":"10.3390","volume":"26","author":[{"given":"Liang","family":"Zhang","sequence":"first","affiliation":[{"name":"Graduate School, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueqiu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-7135","authenticated-orcid":false,"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Equipment Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109554","DOI":"10.1016\/j.ress.2023.109554","article-title":"Multi-scale collision risk estimation for maritime traffic in complex port waters","volume":"240","author":"Xin","year":"2023","journal-title":"Reliab. 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