# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 2 YOLOX Network Architecture  
YOLOX is a single-stage object detection algorithm, proposed in 2021 by [PERSON] et al. (2025). It is built upon YOLOv5 and consists mainly of an input end, backbone network, neck network, and Prediction detection layer, as illustrated in Figure 1.  
Input End: At the input end of the network, YOLOX adopts the Mosaic data augmentation method. This method involves randomly scaling, cropping, and arranging four training images for mixed splicing. The four images are merged into a new image, which is then fed into the neural network model for training. Each of the images involved in the splicing process has annotated bounding boxes. This approach effectively utilizes information from the training set, allowing the model to better understand and recognize various objects and scenes. An illustrative diagram of the splicing effect is shown in Figure 2.  
Figure 1: YOLOX network structure.  
Backbone Network: The backbone network serves as the main network and adopts the network structure of Darknet53 [26]. Darknet53 is a network structure with 53 convolutional layers. By stacking multiple convolutional and pooling layers, it gradually transforms the input image into feature maps of different scales. These feature maps can contain more semantic information, such as object shapes, textures, and colors.  
Neck Network: The neck network functions as the feature fusion network, utilizing the FPN (Feature Pyramid Network) + PAN (Path Aggregation Network) structure for multi-scale feature fusion. The FPN is a top-down structure [27], merging high-level feature maps with upsampled low-level feature maps to create new feature maps conveying strong semantic features. PAN is a bottom-up structure [28], conveying localization information from the bottom layers through downsampling and enabling top-level feature maps to include positional information.  
Prediction Detection Layer: The Prediction detection layer performs multi-scale object detection on the feature maps that are extracted by the neck network. By using detection heads of different sizes, it achieves accurate predictions for the image, generating bounding box coordinates and predicting categories.  
Compared to YOLOv5, YOLOX introduces innovations such as the decoupled detection head, Anchor-Free, and SimOTA. In YOLOv5, the detection head predicts both classification and localization tasks simultaneously using a \(1\times 1\) convolution. In contrast, YOLOX employs a decoupled detection head. Initially, it uses a \(1\times 1\) convolution for dimension reduction, followed by two separate branches, each using a \(3\times 3\) convolution. This structure not only enhances the detection performance but also accelerates the convergence speed. The Anchor-Free algorithm eliminates the need for anchor points and directly predicts the bounding box position and size for object detection. This approach adapts better to the diversity and variability of objects while reducing the computational complexity. SimOTA is a target tracking algorithm that improves the tracking accuracy and robustness by matching positive samples of the target with surrounding negative samples.