A Correction-Based Dynamic Enhancement Framework towards Underwater Detection
This work addresses underwater object detection by optimizing image enhancement for utility rather than visual quality, though it appears incremental as it builds on existing enhancement methods.
The paper tackled the problem of underwater object detection by proposing a lightweight dynamic enhancement algorithm that uses a contribution dictionary to guide low-level corrections, resulting in improved generalization and real-time performance in experiments.
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.