Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as haze-like effects, low visibility, and color distortions. Therefore, we commit to resolving the issue of underwater object detection with compounded environmental degradations. Typical approaches attempt to develop sophisticated deep architecture to generate high-quality images or features. However, these methods are only work for limited ranges because imaging factors are either unstable, too sensitive, or compounded. Unlike these approaches catering for high-quality images or features, this paper seeks transferable prior knowledge from detector-friendly images. The prior guides detectors removing degradations that interfere with detection. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps while the lightly degraded regions of them overlap each other. Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater- images, and make the mapping as a heavily degraded prior (HDP) for underwater detection. Since the statistical properties are independent to image content, HDP can be learned without the supervision of semantic labels and plugged into popular CNNbased feature extraction networks to improve their performance on underwater object detection. Without bells and whistles, evaluations on URPC2020 and UODD show that our methods outperform CNN-based detectors by a large margin. Our method with higher speeds and less parameters still performs better than transformer-based detectors. Our code and DFUI dataset can be found in https://github.com/xiaoDetection/Learning-Heavily-Degraed-Prior.

翻译：水下目标检测由于成像过程受距离和波长影响，产生如雾状效应、低可见度和颜色失真等明显的图像质量退化，导致检测性能低下。因此，我们致力于解决复合环境退化下的水下目标检测问题。典型方法尝试通过开发复杂深度架构生成高质量图像或特征，但这些方法仅在有限范围内有效，因为成像因素要么不稳定、过于敏感，要么相互复合。与这些追求高质量图像或特征的方法不同，本文从检测友好图像中挖掘可迁移的先验知识。该先验引导检测器移除干扰检测的退化效应，其基于统计观察：检测友好图像（DFUI）与水下图像的重度退化区域存在显著特征分布差异，而轻退化区域则相互重叠。为此，我们提出残差特征迁移模块（RFTM），学习检测友好图像与水下图像重度退化块深度表征之间的映射，并将该映射作为水下检测的重度退化先验（HDP）。由于该统计特性与图像内容无关，HDP无需语义标签监督即可学习，并可嵌入主流CNN特征提取网络以提升水下目标检测性能。无需额外复杂设计，在URPC2020和UODD上的评估表明，我们的方法以较大优势超越CNN检测器；且以更高速度、更少参数仍优于基于Transformer的检测器。相关代码及DFUI数据集已开源：https://github.com/xiaoDetection/Learning-Heavily-Degraed-Prior