A Correction-Based Dynamic Enhancement Framework towards Underwater Detection

TL;DR

This paper introduces a lightweight, dynamic image enhancement framework tailored for underwater object detection, optimizing enhancement for detection utility rather than visual quality, and demonstrating superior real-time performance.

Contribution

A novel correction-based dynamic enhancement algorithm using a contribution dictionary to improve underwater detection performance efficiently.

Findings

Outperforms existing methods in real underwater detection tasks.

Balances enhancement effectiveness with computational efficiency.

Demonstrates strong generalization across different underwater scenarios.

Abstract

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.