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Underwater object detection is a crucial and challenging problem in marineengineering and aquatic robot. The difficulty is partly because of thedegradation of underwater images caused by light selective absorption andscattering. Intuitively, enhancing underwater images can benefit high-levelapplications like underwater object detection. However, it is still unclearwhether all object detectors need underwater image enhancement aspre-processing. We therefore pose the questions "Does underwater imageenhancement really improve underwater object detection?" and "How doesunderwater image enhancement contribute to underwater object detection?". Withthese two questions, we conduct extensive studies. Specifically, we use 18state-of-the-art underwater image enhancement algorithms, covering traditional,CNN-based, and GAN-based algorithms, to pre-process underwater object detectiondata. Then, we retrain 7 popular deep learning-based object detectors using thecorresponding results enhanced by different algorithms, obtaining 126underwater object detection models. Coupled with 7 object detection modelsretrained using raw underwater images, we employ these 133 models tocomprehensively analyze the effect of underwater image enhancement onunderwater object detection. We expect this study can provide sufficientexploration to answer the aforementioned questions and draw more attention ofthe community to the joint problem of underwater image enhancement andunderwater object detection. The pre-trained models and results are publiclyavailable and will be regularly updated. Project page:https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection.

Is Underwater Image Enhancement All Object Detectors Need?