文献类型：期刊文献

英文题名：SF-YOLO: A Physics-Guided Framework for Ship Detection in Foggy Maritime Scenarios

作者：Yang, Zhou[1,2];Wu, Tujie[1,2];Deng, Ruoling[1,2,3];Chu, Hubo[1,2,3];Liu, Haitao[1,2,3]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Engn Technol Res Ctr Ocean Equipment & M, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518120, Peoples R China

年份：2026

卷号：14

期号：14

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001832138600001)、、EI(收录号：20263121199419)、Scopus(收录号：2-s2.0-105045828342)、WOS

基金：This work was supported by the Key Project of the Department of Education of Guangdong Province [No. 2023ZDZX1005], the Guangdong Basic and Applied Basic Research Foundation [No. 2024A1515011345], the Innovation Team Project for Ordinary Universities in Guangdong Province [No. 2024KCXTD041], the National Natural Science Foundation of China [Nos. 52171346 and 62171143], the Science and Technology Planning Project of Zhanjiang City [No. 2021A05023], and the Youth S&T Talent Support Program of Guangdong Provincial Association for Science and Technology [No. SKXRC2025396].

语种：英文

外文关键词：ship detection; atmospheric scattering model; YOLOv12; foggy maritime scenarios

外文摘要：Foggy ship detection frequently suffers from image degradation, blurred object contours and a high missed detection rate. Moreover, most existing maritime datasets lack adequate real fog samples. To solve the above problems, in this paper, the Fog-SMD is constructed on the basis of atmospheric scattering principles and fractal theory in combination with a diffusion model to enrich samples covering various fog scenarios. On this basis, we develop an improved SF-YOLO model that takes YOLOv12 as the basic framework. By embedding the shallow-deep adaptive feature fusion module, scattering-guided refinement module and spatial-frequency dual feature attention module, the model can effectively alleviate feature loss resulting from image degradation in foggy environments. Weighted-EIoU loss is introduced to optimize the bounding box regression and reduce the localization deviation of slender ship targets. The experimental results show that SF-YOLO achieves mAP@50 and mAP@50:95 values of 79.3% and 61.6%, respectively, and outperforms mainstream detection algorithms; compared with YOLOv12n, it improves mAP@50:95 from 59.6% to 61.6%, with only a slight increase in parameters from 2.5 M to 2.8 M, providing a new solution for the practical deployment of detection systems and all-weather maritime monitoring in low-visibility foggy scenarios.