Domain-Adaptive Underwater Target Detection Method Based on GPA + CBAM
Year: 2024
Type: article
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.
Authors: Qidong LIU, Xin SHEN, Hailu LIU, Lu CONG, Xianping FU
Institution: Dalian Maritime University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6884716054
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