A gated cross-domain collaborative network for underwater object detection

• Published in: Pattern recognition Vol. 149; p. 110222
• Main Authors: Dai, Linhui, Liu, Hong, Song, Pinhao, Liu, Mengyuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024
• Subjects: Cross-domain; Underwater image enhancement; Underwater object detection; Underwater image enhancement; Underwater object detection; Cross-domain
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.110222

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. •The underwater object detection (UOD) suffers from low contrast, blur, and color shift. We propose a Gated Cross-domain Collaborative network (GCC-Net) to address these challenges. Our method integrates the under- water image enhancement and the underwater object detection methods from a new perspective.•We design a novel cross-domain feature interaction (CFI) module, which can facilitate feature interaction and explore complementary information between the enhanced and raw images.•In addition, a gated feature fusion (GFF) mechanism is proposed to adaptively control the fusion ratio of cross-domain information, thereby avoiding contamination from some unreliable generated results.•The proposed GCC-Net achieves state-of-the-art performance compared to recent UOD methods on the underwater datasets (DUO, Brackish, Trash- Can, and WPBB datasets).