# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 4 Experiments
### Ablation Experiments  
In order to validate the effectiveness of each improvement module, detailed ablation experiments were conducted in this study. Experiment 1 involved the YOLOXs network; Experiment 2 introduced Ghost Conv and Ghost Bottleneck into the backbone network based on Experiment 1; Experiment 3 introduced a CoT block into the feature fusion network based on Experiment 2; Experiment 4 represented the final improved network, replacing the IoU Loss function with Focal_EIoU Loss based on Experiment 3. Table 5 shows the impact of different improvement strategies on the model's detection performance, while Table 6 demonstrates the influence of different improvement strategies on the model's complexity. Based on the comprehensive analysis of the results, it is evident that with the introduction of Ghost Conv and Ghost Bottleneck into the backbone network, although the mAP value decreased by 0.6%, there were significant reductions in parameters, computations, and model size. Specifically, the parameter count decreased by 1.99M, representing a reduction of approximately 24.47%, while the computation decreased by 5.7, with a reduction of approximately 26.39%. Moreover, the model size decreased by 3.7, indicating a reduction of around 23.87%. This suggests that such a strategy effectively addresses the limited memory capacity and computational resources of small underwater devices, meeting the requirements of practical applications. Upon introducing the CoT block into the feature fusion network, the mAP value increased by 0.8%. This indicates that this strategy, by incorporating contextual information, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Ghost Conv,**} & \multirow{2}{*}{**CoT Block**} & \multirow{2}{*}{**Focal\_EIoU Loss**} & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**Model Size/MB**} \\  & & & & & & **Size/MB** \\ \hline
1 & & & & 21.6 & 15.5 \\
2 & \(\surd\) & & & 15.9 & 11.8 \\
3 & \(\surd\) & \(\surd\) & & 15.9 & 11.8 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & 15.9 & 11.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of complexity of different improved models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Ghost Conv,**} & \multirow{2}{*}{**CoT Block**} & \multirow{2}{*}{**Focal\_EIoU Loss**} & \multirow{2}{*}{**P/\%**} & \multirow{2}{*}{**R/\%**} & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} & \multirow{2}{*}{**Params/M**} & \multirow{2}{*}{**FPS**} \\  & & & & 87.1 & 77.8 & 83.3 & 8.05 & 83.3 \\
2 & \(\surd\) & & 90.6 & 74.7 & 82.7 & 6.08 & 77.1 \\
3 & \(\surd\) & \(\surd\) & & 86.7 & 76.6 & 83.5 & 6.06 & 65 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & 90.8 & 77 & 84.4 & 6.06 assists the model in better understanding the position and semantic information of target objects in the entire image, thereby improving the accuracy of the target localization. However, there was a significant decrease in FPS, possibly due to the Contextual Transformer module needing to capture long-range dependencies in the input data, leading to increased computation steps and time, resulting in the decrease in FPS. Upon replacing the IoU Loss function with Focal_EIoU Loss, the mAP value increased by 0.9%. This suggests that Focal_EIoU Loss, by introducing a more accurate measure of target localization, can better optimize the object detection model and improve the detection performance. In summary, compared to the initial model, the improved YOLOXs model achieved a 1.1% increase in mAP value, a reduction of 2.01M parameters, a decrease of 5.7 GFLOPs, and a reduction in model size of 3.7 MB. According to the experimental results, four sets of PR (precision-recall) curve graphs were plotted, as shown in Figure 12. The four graphs, respectively, display the PR curves for each class and the average PR curve for all classes. It can be observed that the area enclosed by the PR curve of the improved YOLOX is larger than that of the other four methods.