# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 2 Related Works
### YOLOv9 Object Detection Network  
YOLOv9, which was released on 21 February 2024, represents the most recent version of the YOLO detection network [21]. It introduces an innovative concept of programmable gradient information, upon which a novel Efficient Layer Aggregation Network (ELAN) structure is formulated for feature extraction, in Figure 1. This architectural design aims to mitigate the problem of information degradation during data propagation within deep networks. Through the utilization of reduced parameters and computational resources, it attains detection accuracy that is on par with or surpasses that of the pre-existing YOLO models.  
The input end (Input), in alignment with YOLOv7, maintains consistency by utilizing Mosaic data augmentation, adaptive anchor box computation, and adaptive image scaling for preprocessing input images to improve the quality of data provided to the model. The Auxiliary Reversible Branch, which leverages the CBNet composite backbone network, integrates a network between itself and the main branch to the aggregation and flow of gradient information. This integration ensures the preservation of deep features crucial for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Num** & **Train** & **Valid** & **Test** & _[EMAIL_ADDRESS] (\%)_ \\ \hline Faster R-CNN (VGG16) & 4757 & 3805 & 0 & 952 & 66.5 \\ \hline SSD (VGG16) & 4757 & 3805 & 0 & 952 & 70.2 \\ \hline FCOS (ResNet50) & 4757 & 3805 & 0 & 952 & 72.3 \\ \hline Faster R-CNN (ResNet50) & 4757 & 3805 & 0 & 952 & 73.2 \\ \hline FreeAnchor (ResNet50) & 4757 & 3805 & 0 & 952 & 74.8 \\ \hline EfficientDet & 4757 & 3805 & 0 & 952 & 74.8 \\ \hline CenterNet (ResNet50) & 4757 & 3805 & 0 & 952 & 76.1 \\ \hline YOLOv8n & 4707 & 3765 & 0 & 942 & 86.1 \\ \hline DDA + YOLOv7-ACmix & 4707 & 3765 & 0 & 942 & 87.2 \\ \hline YOLOv8-MU & 4707 & 3765 & 0 & 942 & 88.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of _[EMAIL_ADDRESS] for four object classes on the URPC2019 dataset.  
Figure 1: YOLOv9 network structure.  
object detection and helps in reducing information loss during the feedforward processes of the network. Modifications to the Backbone network primarily entail amalgamating features of CSPNet and ELAN to formulate a Generalized ELAN (GELAN) as the feature extraction unit. RepConv module acts as the foundational convolutional module, employing reparameterization techniques to further augment feature extraction capabilities. The section continues to utilize Feature Pyramid Network (FPN) + Path Aggregation Network (PAN) structured PAN but replaces the E-ELAN module with a GELAN layer. At the output end (Prediction), the model continues the practice of employing three different prediction boxes to encompass information on the classification, location, and confidence of targets. The process concludes with non-maximum suppression to eliminate redundant boxes. However, in conjunction with the information flow of the Auxiliary Reversible Branch, improvements have been implemented in the information fusion process.  
The YOLOv9 algorithm was originally developed for object detection tasks in various scenarios. To optimize the network for learning and deployment, specific improvements have been implemented to reduce the complexity and improve its ability to detect bounding boxes.