Unsupervised domain adaptation framework with global-local adversarial learning and masked image consistency for fish counting in deep-sea aquaculture

Abstract

Accurate fish counting is crucial for effective management in deep-sea aquaculture. However, the diverse deep-sea aquaculture environments pose significant challenges to the generalizability of fish counting models. Current methods rely heavily on extensive labeled datasets and struggle to adapt to unseen scenarios. To address these limitations, this study proposes an unsupervised domain adaptation framework that leverages global-local adversarial learning and masked image consistency for cross-domain fish counting. A global-local discriminator is designed to extract the domain-invariant features by aligning the density map prediction across domains at the image and patch levels. Additionally, a masked image consistency module is designed to enhance the utilization of spatial context in the target image by enforcing prediction consistency between masked and complete target images. To validate our approach, we established four fish counting datasets using cameras with varying lighting conditions and viewpoints from two actual deep-sea cages. The framework was evaluated on three domain adaptation tasks: (1) natural to artificial lighting, (2) fixed to free viewpoints, and (3) “Shenlan 1” to “Genghai 1” cages. Experimental results demonstrate that the proposed method significantly improves generalization to diverse aquaculture scenarios without requiring additional data annotations. It outperformed state-of-the-art methods, reducing the mean absolute percentage error by 17.34 % for cross-view counting and 6.77 % for cross-cage counting.