# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 3 Methods  
### Improvement Strategies for the Backbone Network  
The backbone network of YOLOX consists of CSPDarknet53 and SPP (Spatial Pyramid Pooling) [29] networks. The design of the entire backbone network aims to organically combine these components to capture features at different scales and levels comprehensively. This enhances the accuracy and robustness of the object detection. The architecture is shown in Table 1.  
Figure 2: Mosaic data augmentation splicing effect illustration. Firstly, the image undergoes processing through the Focus network, which segments the image into sub-feature maps of different sizes. This helps capture detailed information about the target at various scales. Subsequently, each sub-feature map undergoes a series of operations, including three sets of Conv layers and the CSPBottleneck_1 layer. The role of the Conv layers is to extract features from the image, reducing the size of the feature map by half and doubling the number of channels, thereby enhancing the network's perception of the target. The CSPBottleneck_1 layer divides the feature map into two branches, as illustrated in Figure 3. One branch undergoes channel dimension transformation through a 1 \(\times\) 1 Conv layer, then passes through a bottleneck1 with a residual structure, and finally concatenates with the other branch, processed through a 1 \(\times\) 1 Conv layer. The concatenated features then undergo further processing through a 1 \(\times\) 1 Conv layer. Lastly, the feature map undergoes processing through Conv layers, the SPP network, and the CSPBottleneck_1 layer. The SPP network consists of four branches, as shown in Figure 4. The first branch serves as the direct output, while the remaining three branches perform pooling operations on the feature map using differently sized pooling layers (5 \(\times\) 5, 9 \(\times\) 9, 13 \(\times\) 13). The outputs of these branches are ultimately merged to achieve multi-scale feature fusion, thereby enhancing the model's detection capability for targets. In order to reduce the model's parameter count and computational load, this paper introduces GhostConv (Ghost Convolution) and GhostBottleneck into the backbone network [30], replacing the standard convolutions in the YOLOX network and the Bottleneck1 structure in CSPBottleneck_1. The architecture is shown in Table 2.  
Due to the redundancy and similarity of features that are often present in feature maps generated by standard convolutions, [PERSON] et al. proposed an innovative structure called the Ghost Module to address this issue. The Ghost Module significantly reduces the model's complexity without reducing the number of feature maps. The schematic diagram of this structure is shown in Figure 5. Firstly, intrinsic feature maps are extracted using a \(1\times 1\) convolution layer. Then, more economical linear operations are applied to generate the remaining feature maps using a \(5\times 5\) convolution layer. Finally, the outputs of these two layers are concatenated to form the final feature map of the Ghost Module. The Ghost Bottleneck is divided into two structures based on the stride, as shown in Figure 6. The Bottleneck with a stride of 1 consists of two concatenated Ghost Modules. The first Ghost Module is used to expand the number of channels, and the second Ghost Module reduces the number of channels to match the input channel count. This structure is employed to increase the network's depth without compressing the height and width of the input feature layer. The Bottleneck with a stride of 2 introduces a Deepwise convolution with a stride of 2 between the two Ghost Modules, compressing the height and width of the input feature layer and altering its shape. Since the CSPBottleneck_1 in the YOLOX model uses a convolution with a stride of 1, the Ghost Bottleneck in this paper adopts a structure with a stride of 1.