To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance.

翻译：为了帮助探测水下物体,提高图像技术往往被用作预处理步骤,但大多数现有增强方法倾向于追求图像的视觉质量,而不是为探测任务提供有效的帮助。事实上,图像增强算法应当优化,目标是改进效用。在本文件中,为了适应水下探测任务,我们建议使用一个贡献字典来指导低水平校正。动态解决方案旨在捕捉探测偏好方面的差异。此外,它还可以平衡校正作业的贡献与其时间复杂性之间的不一致。实际水下物体探测任务的实验结果显示了我们拟议方法在一般化和实时性能方面的优势。