Domain-Adaptive Underwater Target Detection Method Based on GPA + CBAM

Abstract

Underwater target detection is often more susceptible to domain shift and reduced detection accuracy. In response to this phenomenon, this article proposed a domain-adaptive underwater target detection method based on graph-induced prototype alignment(GPA). GPA obtained instance-level features in the image through graph-based information propagation between region proposals and then derived prototype representations for category-level domain alignment. The above operations could effectively aggregate different modal information of underwater targets, thereby achieving alignment between the source and target domains and reducing the impact of domain shift. In addition, in order to make the neural network focus on instance-level features under different water domain distributions, a convolutional block attention module(CBAM) was added. The experimental results have shown that the proposed method can effectively improve detection accuracy during domain shift.

Keywords

• underwater target detection
• graph-induced prototype alignment
• domain adaptation
• convolutional block attention module