###### Abstract
Additionally, objects at greater depths appear darker and less distinguishable due to light scattering, further complicating the segmentation process. The changes in the appearance of objects, driven by fluctuating lighting conditions, result in a notable decrease in the performance of segmentation models, which often struggle to adapt to the dynamic nature of underwater images. Secondly, underwater image data are often highly imbalanced, which presents another critical challenge. In many natural underwater environments, large schools of fish tend to gather around coral reefs or other structures, resulting in a much higher number of fish instances compared to other objects such as plants, rocks, or debris. This uneven distribution of object categories creates a skewed training dataset, where the model encounters an overwhelming frequency of fish instances in contrast to less common objects. As a result, the model prioritizes the detection of more frequent objects while neglecting or misclassifying rarer ones. This data imbalance negatively impacts segmentation performance, especially for less frequent objects, as the model struggles to learn to recognize them accurately. The problem becomes more complex when infrequent objects appear alongside more common ones in the same frame, further challenging the model's ability to correctly segment these rare instances. In this work, we propose a novel deep learning segmentation model, defined as the AASNet (Agricultural Aqua Segmentation Network), which incorporates a Linear Correlation Attention mechanism and a Dynamic Adaptive Focal Loss, that is specifically optimized to address challenges such as lighting variations, complex backgrounds, and extreme data imbalance in underwater scenes. The model's performance has been extensively evaluated on the UIIS [8] and USIS10K [10] datasets. To summarize, our main contributions are listed as follows:  
1. We design a novel Agricultural Underwater Image Instance Segmentation Model, AASNet, which integrates detection and segmentation functions. This model is specifically optimized to address the challenges of lighting variations and extreme data imbalance in underwater scenes. In AASNet, we introduce the Linear Correlation Attention (LCA) mechanism and a dynamic adaptive loss function. The LCA mechanism captures feature correlations, enhancing the model's ability to adapt to variations in lighting and complex backgrounds. At the same time, the dynamic adaptive loss function addresses the issue of data imbalance, improving classification accuracy and ensuring that the model maintains high performance across diverse underwater scenarios. 3. Our method achieves state-of-the-art performance on the UIIS and USIS datasets, surpassing current mainstream methods in accuracy, parameter efficiency, and inference speed. The experimental results demonstrate that AASNet excels in handling complex underwater scenes, showcasing its superior performance. The remainder of the paper is organized as follows: Section 2 reviews the related work in underwater image instance segmentation, discussing key approaches and their limitations in addressing the challenges posed by underwater environments. Section 3 presents the methodology behind AASNet, detailing the innovative components of the Linear Correlation Attention mechanism and the Dynamic Adaptive Focal Loss. Section 4 outlines the experimental setup and results, comparing AASNet's performance with state-of-the-art methods on the UIIS and US  
occlusions, and extreme class imbalance in underwater datasets, all of which can adversely affect segmentation performance.