一个基于扩散模型的图像生成框架用于水下物体检测.
Yaoming Zhuang1, Longyu Ma2,3, Jiaming Liu2
1Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China. zhuangyaoming@mail.neu.edu.cn.
Communications engineering
|December 30, 2025
概括
本研究介绍了水下布局导向扩散框架 (ULGF),用于生成各种水下图像用于物体检测. ULGF框架通过从有限的现实世界水下图像中创建高保真度合成数据来提高检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 海洋技术 海洋技术
- 数据增强的数据增强.
背景情况:
- 水下物体检测对于海洋监测和救援至关重要.
- 有限的水下数据和场景多样性阻碍了检测准确度.
- 现有的方法通常依赖于外部数据,从而造成域间隙.
研究的目的:
- 提出一个新的框架,即水下布局导向扩散框架 (ULGF),用于增强水下物体检测数据集.
- 仅使用一小部分现有的水下数据生成高准确度和多样化的水下图像.
- 提高水下物体检测模型的性能.
主要方法:
- 开发了水下布局导向扩散框架 (ULGF),一种扩散模型.
- 该框架仅在有限的水下图像及其标签上运行.
- 没有使用外部或空中数据集成来生成图像.
主要成果:
- 在理论上,ULGF可以生成无限的高保真度和多样化的水下图像.
- 与现有方法相比,生成的图像具有较小的域间隙.
- 在现实世界水下场景中大大提高了对象检测性能.
结论:
- ULGF框架有效地解决了有限的水下数据的挑战.
- 它为水下成像中合成数据生成提供了一种新的方法.
- 该方法显示了推动海洋技术和应用的巨大潜力.
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