{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T04:05:40Z","timestamp":1792814740219,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"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>Ship detection serves as a core foundational task for marine environmental perception. However, in real marine scenarios, dense vessel traffic often causes severe target occlusion while multi-scale targets, asymmetric vessel geometries, and harsh conditions (e.g., haze, low illumination) further degrade image quality. These factors pose significant challenges to vision-based ship detection methods. To address these issues, we propose M2S-YOLOv8, an improved framework based on YOLOv8, which integrates three key enhancements: First, a Multi-Scale Asymmetry-aware Parallelized Patch-wise Attention (MSA-PPA) module is designed in the backbone to strengthen the perception of multi-scale and geometrically asymmetric vessel targets. Second, a Deformable Convolutional Upsampling (DCNUpsample) operator is introduced in the Neck network to enable adaptive feature fusion with high computational efficiency. Third, a Wasserstein-Distance-Based Weighted Normalized CIoU (WA-CIoU) loss function is developed to alleviate gradient imbalance in small-target regression, thereby improving localization stability. Experimental results on the Unmanned Vessel Zhoushan Perception Dataset (UZPD) and the open-source Singapore Maritime Dataset (SMD) demonstrate that M2S-YOLOv8 achieves a balanced performance between lightweight design and real-time inference, showcasing strong potential for reliable deployment on edge devices of unmanned marine platforms.<\/jats:p>","DOI":"10.3390\/s26020502","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T14:57:25Z","timestamp":1768229845000},"page":"502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["M2S-YOLOv8: Multi-Scale and Asymmetry-Aware Ship Detection for Marine Environments"],"prefix":"10.3390","volume":"26","author":[{"given":"Peizheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"China E-Tech(Ningbo)Maritime Electronics Research Institute Co., Ltd., Ningbo 315000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3392-8844","authenticated-orcid":false,"given":"Dayong","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyi","family":"Mu","sequence":"additional","affiliation":[{"name":"China E-Tech(Ningbo)Maritime Electronics Research Institute Co., Ltd., Ningbo 315000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1460-0677","authenticated-orcid":false,"given":"Linlin","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xiao, G., Wang, Y., Wu, R., Li, J., and Cai, Z. 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