面向无人艇视角的海上光学图像目标检测算法研究

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中图分类号：E917 文献标志码：A DOI：10.3969/j. issn.1673-3819.2025.05.007

Abstract：During theapplication of unmanned surface vehicles （USVs）innatural sea areas，issuessuch as low-quality opticalimageimaging，localizedtargetfeaturesanddepth-directionaldistortioncausedbyside-viewingangles，lensdeforation duetosaltwaterdroplets，andthecoexistenceofextremelylargeandsmalltargetsocur.Thesephenomenaleadtoadecline intheperformanceofdeeplearning-basedimage targetdetectionalgorithms，resulting inhighratesofmisseddetectiosand falsealarms.Consequently，thiscauses thecolisionavoidancedecision-makingalgorithms todivergeortriggerfrequentemergencyalarms.To enhanceobjectdetection acuracyfor USVoptical images，，this paper proposes anoptimizedframework basedontheYOLOv8 model.First，theCopy-Pastealgorithmandunpaired image style transferalgorithmare introduced to generate multi-viewtargetimagesandlow-qualityimagescausedbyadverseenvironmentalconditions，addressing theimbalanceintheproportionoflow-qualityimagesinthedataset.Second，adedicateddetectionheadforsmalltargetsisadedto thedetectionnetwork，andthelossfunctionisoptimizedtoenhancethedetectioncapabilityforsmalltargetswhilepreserving theoriginal model’sperformanceonlargerobjects.Thenewlydevelopedobjectdetection model，trainedontheaugmented dataset，achieves an average precision of 96.2% on the test set.

Keywords：USV；YOLOv8；data augmentation;target detection

随着人工智能、传感器技术等飞速发展和深度应用，自主无人艇得以蓬勃发展，成为海洋开发与利用的新质平台。（剩余13121字）