# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 2 Related Works
### Attention Mechanism  
In the research of attention mechanisms for visual tasks, various methods have been developed to optimize feature representation across different dimensions. Conventional channel attention approaches, such as Squeeze-and-Excitation (SE) [43], capture inter-channel relationships by utilizing global pooling and fully connected layers, but their neglect of spatial information limits localization capabilities in complex scenes. Subsequent improvements, such as Coordinate Attention (CA) [44], attempt to embed spatial coordinate information into channel weights, while Efficient Channel Attention (ECA) [45] reduces parameters through 1D convolution. However, these methods are still constrained by  
Figure 1: YOLOv8 network structure.  
static weight allocation patterns, making it difficult to adapt to geometric deformations or occlusions of targets. Spatial-channel joint attention mechanisms, such as Convolutional Block Attention Module (CBAM) [46], integrates both channel-wise and spatial attention, but their reliance on fixed pooling operations to generate spatial weights limits their ability to model irregular targets. The parameter-free SimAM [47] module implicitly models feature importance through an energy function, but its static attention mechanism lacks flexibility in dynamic scenes.  
In recent years, dynamic sparse attention mechanisms have gradually become a research focus. For example, Efficient Multi-Scale Attention (EMA) [48] enhances multi-scale feature fusion through channel grouping and cross-dimensional interaction, but its fixed grouping strategy may not adapt to dramatic changes in target scale; Bilinear Routing Attention (BRA) [49] dynamically selects relevant regions through two-layer routing, but the routing process still relies on predefined sparse patterns. In contrast, deformable attention mechanisms [50] dynamically adjust feature sampling positions through learnable spatial offsets, achieving adaptive focus on input content. The core idea is to use the network to predict offsets for feature sampling positions, enabling attention computation to bypass irrelevant background regions (such as underwater suspended particles or lighting interference) and directly capture key local features of targets. This dynamic deformation capability gives it unique advantages in complex underwater scenarios: on the one hand, through offset learning, deformable attention can compensate for geometric deformations caused by water refraction or target motion, enhancing localization robustness for blurred or distorted targets; on the other hand, its sparse attention mechanism performs intensive computation only in predicted key regions, effectively suppressing noise interference from complex underwater backgrounds. Compared to traditional attention mechanisms' reliance on fixed patterns (such as CA's coordinate encoding or EMA's multi-scale grouping), the data-driven deformation characteristics of deformable attention are more suitable for non-rigid deformations, low contrast, and occlusion challenges of underwater targets, providing a more universal feature enhancement solution for underwater detection tasks.