Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.

中文翻译：

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具有主动成像功能的基于光学先验的水下目标检测

水下物体检测在研究和实践中起着重要作用，因为它提供了代表水下物体的浓缩信息内容。但是，从水下图像中检测物体具有挑战性，因为水下环境会大大降低图像质量并扭曲物体与背景之间的对比度。为了解决这个问题，本文提出了一种基于光学先验的水下物体检测方法，该方法利用光学原理在水下图像上识别光学准直，为提取物体特征提供了有价值的指导。与数据驱动的知识不同，我们方法中的先验方法独立于训练样本。我们方法的根本新颖之处在于图像先验和目标检测任务的集成。