# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 3 Materials and Methods
### The Proposed Detection Model, SUD-YOLO
#### 3.2.2 Receptive-Field Attention and Local Feature Fusion Module  
[PERSON] et al. proposed Efficient Local Attention (ELA) [34], which is designed to overcome issues in existing attention mechanisms, such as the insufficient use of spatial information, information loss caused by reduced channel dimensions, and the high complexity of the attention generation process.  
[PERSON] et al. proposed Receptive Field Block (RFB) [35], which simulates the relationship between the receptive field size and eccentricity in the human visual system. It captures multi-scale information by utilizing multi-branch convolutional layers and dilated convolutional layers, thereby enhancing the feature representation capability of lightweight CNN models. Each branch uses kernels of different sizes combined with specific dilation rates to adjust the eccentricity of the receptive field, and the outputs from all branches are concatenated and integrated through a \(1\times 1\) convolution layer, generating a receptive field configuration similar to that of the human visual system. The RFB module is designed to improve feature discriminability and robustness, and it can be easily integrated into existing CNN architectures, such as the SSD detector, thereby enhancing detection accuracy without adding excessive computational cost.  
[PERSON] et al. proposed the receptive-field attention (RFA) mechanism [36] and the corresponding convolution operation RFAConv, which focuses on the receptive field's spatial features to address the issue of parameter sharing in large convolution kernels. This approach significantly improves network performance with almost no increase in computational cost.  
Inspired by these, we designed the Receptive-Field Attention and Local Spatial Feature Fusion Convolution Module (RFEConv). The structure of this module is shown in Figure 5. First, 3 \(\times\) 3 grouped convolutions are used to quickly extract receptive field features. The normalization and ReLU activation functions are applied to accelerate convergence speed and improve the expressive ability of the network. Next, the generated feature maps are reshaped, and the input feature map is globally average-pooled in both the width and height directions to obtain feature maps in these two directions. A one-dimensional convolution is then applied to process the sequential signals, followed by GroupNorm for normalization. After passing through a sigmoid activation function, local spatial feature weights (w) are obtained. These weights are used to perform a weighted summation on the reshaped feature maps, and finally, a 3 \(\times\) 3 convolution is applied to generate the output feature map.  
RFEConv particularly focuses on the spatial features of the receptive field, not limited to traditional spatial dimensions, enabling the network to more effectively understand and process local regions in images, thereby improving the precision of feature extraction. RFEConv dynamically generates receptive field feature spaces and adaptively adjusts the shape and range of the receptive field according to the size of the convolution kernels, so as to accommodate aquatic organisms of different sizes. A smaller receptive field is generated for smaller aquatic organisms to preserve finer details, while a larger receptive field is generated for larger aquatic organisms to capture global features.