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：为提升水下目标检测的性能，图像增强技术常被用作预处理步骤。然而，现有大多数增强方法倾向于追求图像的视觉质量，而非为检测任务提供有效帮助。实际上，图像增强算法应以效用提升为目标进行优化。本文为适应水下检测任务，提出了一种轻量级动态增强算法，利用贡献字典指导低级校正。动态方案的设计旨在捕捉检测偏好的差异，同时能平衡校正操作的贡献与其时间复杂度之间的不一致性。真实水下目标检测任务的实验结果表明，本文方法在泛化能力和实时性能方面均具有优越性。