Is Underwater Image Enhancement All Object Detectors Need?

• URL: http://arxiv.org/abs/2311.18814v1
• Date: Thu, 30 Nov 2023 18:54:08 GMT
• Title: Is Underwater Image Enhancement All Object Detectors Need?
• Authors: Yudong Wang and Jichang Guo and Wanru He and Huan Gao and Huihui Yue and Zenan Zhang and Chongyi Li
• Abstract summary: It is unclear whether all object detectors need underwater image enhancement as pre-processing. We use 18 state-of-the-art underwater image enhancement algorithms to pre-process underwater object detection data. We retrain 7 popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models.
• Score: 27.909292529992584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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