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Semantic segmentation in aquatic scenes is key technology water environment monitoring. Small-scale object detection and segmentation in aquatic scenes are major challenges in semantic segmentation of water bodies. Current typical semantic segmentation methods often use multi-scale feature fusion operations, features of different scales from different network layers are aggregated, enabling the features to have both strong semantic representation from high-level features and strong feature detail expression capability from low-level features. However, current methods, although they focus on the details of small-scale objects, primarily rely on low-level features to determine the presence of objects in the network scale adaptation for small object detection, resulting in the loss of accuracy when using high-level semantic features for prediction. Moreover, cross-scale fusion does not depend on category characteristics. Therefore, existing methods are not ideal for semantic-constrained small object segmentation, such as water surface garbage and plant debris. Our method focuses on the cross-level semantic information aggregation and utilization for object segmentation in aquatic scenes, providing a new approach for small object segmentation in complex semantic environments. In aquatic scenes, the category of objects has strong contextual relevance. Therefore, this paper proposes a cross-level semantic aggregation network to address the problem of small object segmentation in aquatic scenes. The cross-level semantic aggregation method guides the high-level features to perform semantic aggregation using low-level features, enabling the aggregation of features with high-level semantic features of the same category as small objects, while introducing relevant contextual scene features of different categories. Compared to traditional scale fusion, this introduces a new aggregation method within the semantic framework to handle small object segmentation in complex contextual relationships. We conducted extensive experiments on our self-built water body scene dataset, ColorWater, and the public dataset Aeroscapes. In addition to achieving state-of-the-art performance in overall segmentation, we particularly achieved significant advantages in small object categories such as floating garbage on the water surface and plant debris, which are the focus of this paper.
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