  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.
