基于优化YOLOv8模型的海上船舶目标检测研究
1Xijing University, Xi'an, 710123, Shaanxi, China. 565200245@qq.com.
Scientific reports
|November 18, 2025
概括
本研究介绍了YOLOv8_optimize,这是海上船舶检测的增强模型. 它通过优化YOLOv8架构并采用焦点损失来提高海洋监视的准确性来实现卓越的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 海洋技术 海洋技术
背景情况:
- 准确的海上船舶检测对于海洋监视至关重要.
- 现有的方法在多样化和复杂的海洋环境中面临挑战.
研究的目的:
- 开发一个改进的YOLOv8模型,命名为YOLOv8_optimize,用于增强海上船舶检测.
- 提高船舶检测系统的效率和稳定性.
主要方法:
- 构建了一个大型数据集,包含超过8万张注释的海上图像.
- 通过集成MBConv模块和深度可分离卷曲来优化YOLOv8骨干.
- 改进了使用焦点损失来解决类不平衡的检测头.
主要成果:
- 与YOLOv8n和YOLOv8s相比,YOLOv8_optimize模型表现出优越的性能.
- 在保持高检测精度的同时,在计算复杂性方面实现了显著的减少.
- 改善了难以采集的样本和稀有船只类别的优先级.
结论:
- YOLOv8_optimize为海上船舶检测提供了一种高效,强大的解决方案.
- 该模型在海洋监视应用中具有实质性的实际价值.
- 应用的优化提高了运营效率和检测性能.
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