论文标题
水下图像增强和水下对象检测的基准数据集
A Benchmark dataset for both underwater image enhancement and underwater object detection
论文作者
论文摘要
水下图像增强是如此重要的视觉任务，因为它在海洋工程和水生机器人中的重要性。通常，这是提高水下对象检测等高级视力任务的性能的预处理步骤。即使许多以前的作品都表明水下图像增强算法可以提高检测器的检测准确性，但也没有专门针对研究这两个任务之间关系的工作。这主要是因为现有的水下数据集缺乏基于检测准确性或图像质量评估指标的界限框注释或高质量参考图像。为了研究水下图像增强方法如何影响以下水下对象检测任务，在本文中，我们提供了一个大规模的水下对象检测数据集，其中既有界限框注释又有高质量的参考图像，即OUC数据集。 OUC数据集为研究人员提供了一个平台，以全面研究水下图像增强算法对水下对象检测任务的影响。
Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the detection accuracy of the detectors, no work specially focus on investigating the relationship between these two tasks. This is mainly because existing underwater datasets lack either bounding box annotations or high quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset. The OUC dataset provides a platform for researchers to comprehensive study the influence of underwater image enhancement algorithms on the underwater object detection task.
