A Gated Cross-domain Collaborative Network for Underwater Object
  Detection
Linhui Dai Hong Liu Pinhao Song Mengyuan Liu
Underwater object detection (UOD) plays a significant role in aquaculture and
marine environmental protection. Considering the challenges posed by low
contrast and low-light conditions in underwater environments, several
underwater image enhancement (UIE) methods have been proposed to improve the
quality of underwater images. However, only using the enhanced images does not
improve the performance of UOD, since it may unavoidably remove or alter
critical patterns and details of underwater objects. In contrast, we believe
that exploring the complementary information from the two domains is beneficial
for UOD. The raw image preserves the natural characteristics of the scene and
texture information of the objects, while the enhanced image improves the
visibility of underwater objects. Based on this perspective, we propose a Gated
Cross-domain Collaborative Network (GCC-Net) to address the challenges of poor
visibility and low contrast in underwater environments, which comprises three
dedicated components. Firstly, a real-time UIE method is employed to generate
enhanced images, which can improve the visibility of objects in low-contrast
areas. Secondly, a cross-domain feature interaction module is introduced to
facilitate the interaction and mine complementary information between raw and
enhanced image features. Thirdly, to prevent the contamination of unreliable
generated results, a gated feature fusion module is proposed to adaptively
control the fusion ratio of cross-domain information. Our method presents a new
UOD paradigm from the perspective of cross-domain information interaction and
fusion. Experimental results demonstrate that the proposed GCC-Net achieves
state-of-the-art performance on four underwater datasets.