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多艘船的检测和分类与功能增强和轻量级的核聚变
Ying Han1, Hao Wang2, Nick Renjin3
1Naval Architecture and Port Engineering College, Shandong Jiaotong University, Weihai, 264200, People's Republic of China.
Scientific reports
|October 31, 2025
概括
这项研究使用修改后的YOLOv8算法增强了船舶目标检测,提高了复杂海洋环境中的准确性. 新型号提供更好的特征提取和轻量化融合,可靠的船舶识别.
科学领域:
- 计算机视觉 计算机视觉
- 海洋技术 海洋技术
- 人工智能的人工智能
背景情况:
- 传统的船舶检测方法面临诸如背景干扰和多尺度物体识别等挑战.
- 针对小样本识别的培训数据不足,阻碍了传统技术.
- 确保海上安全和有效的船舶交通管理需要强大的检测系统.
研究的目的:
- 开发一个增强的船舶多目标检测模型,以提高准确性和可靠性.
- 在复杂的海洋环境中解决经典方法的局限性.
- 增强用于对复杂背景进行船舶识别的语义特征的提取.
主要方法:
- 修改了YOLOv8算法作为基线模型 (YOLOv8n).
- 将ESSE模块和GSConvns技术集成到YOLOv8骨干中.
- 整合了Wise-IoU技术,以改善检测.
主要成果:
- 平均检测准确度为82.1% (码头船),99.1% (海上船) 和91.7% (红外离岸船数据集).
- 与基线模型相比,表现出明显的改善.
- 通过IOU和废弃性研究验证了增强的特征提取,轻量级融合和检测能力.
结论:
- 提议的增强型YOLOv8模型显著提高了船舶目标检测性能.
- 这些修改增强了多尺度特征提取和语义特征提取,特别是在具有挑战性的条件下.
- 这种方法有可能提高船舶目标探测系统的自动化,可靠性和质量.
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