重新审视用于物体检测的水下图像增强:一个统一的质量检测评估框架
Ali Awad1, Ashraf Saleem1, Sidike Paheding2
1Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USA.
Journal of imaging
|January 27, 2026
概括
水下图像增强可以提高对象检测,但仅用于低质量的图像. 选择性增强比没有或完全增强提供了显著的收益,指导未来的计算机视觉任务优化.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 水下图像的质量很差 (颜色扭曲,对比度低,可见度降低).
- 图像增强通常使用,但其对对象检测的好处仍在争论中.
- 现有的评估缺乏严谨性和细节性.
研究的目的:
- 综合评估用于物体检测的最新水下图像增强方法.
- 为严格,细致的绩效评估提出一个统一的框架.
- 调查增强对数据集和图像层面的检测准确度的影响.
主要方法:
- 开发了一个统一的评估框架,整合了质量评估 (Q-index) 和每图像检测 (COCO mAP).
- 对现代物体探测器进行了九种增强方法的评估.
- 进行混合集分析以确定选择性增强的理论性能极限.
主要成果:
- 传统的质量指标无法可靠地预测对象检测性能.
- 数据集级别的分析掩盖了显著的图像级别变化.
- 增强可以提高低质量的输入的检测精度,但在其他情况下可能会降低性能.
- 选择性增强比原始和完全增强的数据集产生了实质性的收益.
结论:
- 水下图像增强可以有利于对象检测,当评估细粒度时.
- 选择性增强策略对于优化计算机视觉任务的性能至关重要.
- 这项研究提供了证据,证明了对增强技术的明智应用.
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