ABSTRACT Challenging underwater environmental conditions cause severe degradation of underwater images, including intensity attenuation and colour distortion, leading to incomplete feature representation and posing difficulties for state‐of‐the‐art detectors. To address this issue, we propose a cross‐channel feature fusion network (CCFF‐Net), which innovatively enriches feature representation through complementary feature fusion across multiple channels. The network comprises three key components. First, an adaptive greyscale image generation module is designed to dynamically enhance channel information specialized for object appearance, selectively reinforcing detector‐preferred cues in greyscale representation. Second, a cross‐channel feature fusion module is introduced to facilitate information interaction between greyscale and chromatic channels, compensating for potential feature degradation caused by colour distortion and intensity attenuation by generating complementary and enhanced feature representations. Third, an enhanced feature pyramid network‐based multi‐scale feature fusion module is proposed to improve detection performance by reinforcing feature representations for both small‐scale and occluded objects. Extensive experiments on four public datasets validate the effectiveness of CCFF‐Net, achieving mAP improvements of 2.9%, 2.6%, 4.0% and 1.8% compared to the baseline on the DUO, UODD, UDD and URPC2020 datasets, respectively, demonstrating the superiority and generalization capability of the proposed method.
Chen et al. (Thu,) studied this question.