# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 3 Methods
### Enhanced Neck Structure: SF-PAFPN  
In marine benthos detection tasks, the diminutive size of marine benthos and the significant distance between the camera and targets during underwater data collecting lead to most objects in acquired images being categorized as small objects. Furthermore, the habitat characteristics of marine benthos frequently leads to dense clustering, causing reciprocal overlap and occlusion, which obscures the detailed information of small objects in images, hence complicating detection and resulting in missed and false detections.  
Recent enhancement solutions for small object detection tasks typically incorporate a P2 small object detection layer to preserve more feature information [53; 54]. This method results in substantial computational expenses and prolonged post-processing time. Consequently, to focus on the detailed characteristics of smaller marine benthos objects, ensuring an optimal trade-off between accuracy and efficiency, we have developed an SF-PAFPN neck structure that proficiently amalgamates multi-level feature information. The fused features have better descriptive ability for smaller, occlusion-prone marine benthos objects, which helps the model learn richer feature representations and improve detection performance.  
The shallow feature maps from the P2 layer are first down-sampled via the Focus module and subsequently fused with the output of P3, with the objective of decreasing resolution while more efficiently retaining small object feature information. Following this, utilizing the CSP idea and Omni-Kernel [55], we developed the CSPOKM module to process the previously described fused output, therefore augmenting the representation of small object feature information. The input first undergoes processing through a \(1\times 1\) convolutional layer, after which a Split operation directs 25% of the channels to the Omni-Kernel module, while the remaining channels undergo \(1\times 1\) convolution to mitigate computational complexity. Ultimately, the two are concatenated and subsequently processed through a \(1\times 1\) convolutional layer for feature integration.  
The Omni-Kernel module, depicted in Figure 6, consists of three branches: local, large, and global. The local branch employs \(1\times 1\) depth-wise convolution to enhance compromised local information from small scales. The large branch utilizes three substantial kernel depth-wise convolutions (\(1\times 31\), \(31\times 31\), and \(31\times 1\)) to enhance the receptive field. The global branch integrates a Dual-domain Channel Attention Module (DCAM) and a Frequency-based Spatial Attention Module (FSAM) to facilitate global perception. In this context, DCAM executes crude dual-domain feature enhancement inside the channel dimension, whereas FSAM implements fine-grained augmentation of the spectrum in the spatial dimension, hence augmenting the network's capacity to leverage global information. The outcomes from the three branches are combined by element-wise addition and subsequently refined using a \(1\times 1\) convolution to proficiently acquire feature representations from global to local levels.  
To evaluate the effectiveness of SF-PAFPN, we conducted comparative experiments by integrating the SF-PAFPN module with the P2-based approach. The detailed experimental procedures are outlined in Section 4.2.5, Sub-experiment 2.