Underwater object detection is a crucial and challenging problem in marine engineering and aquatic robot. The difficulty is partly because of the degradation of underwater images caused by light selective absorption and scattering. Intuitively, enhancing underwater images can benefit high-level applications like underwater object detection. However, it is still unclear whether all object detectors need underwater image enhancement as pre-processing. We therefore pose the questions "Does underwater image enhancement really improve underwater object detection?" and "How does underwater image enhancement contribute to underwater object detection?". With these two questions, we conduct extensive studies. Specifically, we use 18 state-of-the-art underwater image enhancement algorithms, covering traditional, CNN-based, and GAN-based algorithms, to pre-process underwater object detection data. Then, we retrain 7 popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models. Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection. We expect this study can provide sufficient exploration to answer the aforementioned questions and draw more attention of the community to the joint problem of underwater image enhancement and underwater object detection. The pre-trained models and results are publicly available and will be regularly updated. Project page: https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection.

翻译：水下目标检测是海洋工程和水下机器人领域中一个关键且具有挑战性的问题。其困难部分源于光的选择性吸收和散射导致的水下图像退化。直观上，增强水下图像可以惠及水下目标检测等高层次应用。然而，目前尚不清楚是否所有目标检测器都需要将水下图像增强作为预处理步骤。因此，我们提出两个问题：“水下图像增强是否真正改善了水下目标检测？”以及“水下图像增强如何促进水下目标检测？”。针对这两个问题，我们开展了广泛研究。具体而言，我们采用18种最先进的水下图像增强算法（涵盖传统方法、基于CNN和基于GAN的算法）对水下目标检测数据进行预处理。随后，我们使用不同算法增强后的结果重新训练7种流行的深度学习目标检测器，获得126个水下目标检测模型。结合使用原始水下图像重新训练的7个目标检测模型，我们利用这133个模型全面分析了水下图像增强对水下目标检测的影响。我们期望这一研究能为上述问题提供充分探索，并引起学界对水下图像增强与水下目标检测联合问题的更多关注。预训练模型和结果已公开并可定期更新。项目页面：https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection。