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Underwater object detection (UOD) plays a significant role in aquaculture andmarine environmental protection. Considering the challenges posed by lowcontrast and low-light conditions in underwater environments, severalunderwater image enhancement (UIE) methods have been proposed to improve thequality of underwater images. However, only using the enhanced images does notimprove the performance of UOD, since it may unavoidably remove or altercritical patterns and details of underwater objects. In contrast, we believethat exploring the complementary information from the two domains is beneficialfor UOD. The raw image preserves the natural characteristics of the scene andtexture information of the objects, while the enhanced image improves thevisibility of underwater objects. Based on this perspective, we propose a GatedCross-domain Collaborative Network (GCC-Net) to address the challenges of poorvisibility and low contrast in underwater environments, which comprises threededicated components. Firstly, a real-time UIE method is employed to generateenhanced images, which can improve the visibility of objects in low-contrastareas. Secondly, a cross-domain feature interaction module is introduced tofacilitate the interaction and mine complementary information between raw andenhanced image features. Thirdly, to prevent the contamination of unreliablegenerated results, a gated feature fusion module is proposed to adaptivelycontrol the fusion ratio of cross-domain information. Our method presents a newUOD paradigm from the perspective of cross-domain information interaction andfusion. Experimental results demonstrate that the proposed GCC-Net achievesstate-of-the-art performance on four underwater datasets.

A Gated Cross-domain Collaborative Network for Underwater Object Detection