# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 5 Conclusions  
Underwater target detection serves as the foundation for underwater robots to achieve automated detection and grasping operations. To facilitate the development and utilization of marine resources, focusing on marine organisms as the detection target, this paper proposes a novel model named YOLOXs-GCE, which is an improvement compared with YOLOXs. Firstly, Ghost Conv and Ghost Bottleneck are introduced into the backbone network, significantly reducing the model's parameter count, computational load, and size, while sacrificing a minimal amount of model accuracy. This lightweight processing method effectively addresses the challenge of underwater image processing under resource-constrained conditions. Subsequently, the CoT block is incorporated into the feature fusion network, enhancing the model's understanding, representation, and generalization capabilities through advantages such as context awareness, parameter sharing, and parallel computing. Finally, IoU Loss is replaced with Focal_EIoU Loss to mitigate the issue of imbalanced positive and negative samples, thereby improving the model's localization ability, prediction performance, and convergence speed. Our experimental results demonstrate that the improved YOLOXs-GCE model achieves an mAP value of 84.4% on the underwater biological dataset detection, with 6.06M parameters, a computational load of 15.9, and a model size of 11.8M.  
This study confirms that YOLOXs-GCE, with its lightweight design and efficient performance, is particularly suitable for underwater equipment with limited resources. It demonstrates outstanding robustness in addressing the variability and challenges of the underwater environment. Looking ahead, we plan to further validate the effectiveness of the YOLOXs-GCE model on a wider range of underwater image datasets and explore the model's application potential in actual underwater environments, such as the autonomous navigation of underwater devices and biodiversity monitoring. Additionally, considering the real-time requirements and resource limitations that may be encountered in practical applications, we will continue to optimize the detection accuracy of the model while reducing its complexity. Although this study has achieved certain results, further improvement in detection accuracy remains a focus of our research. We look forward to pushing the YOLOXs-GCE model into a broader range of underwater application scenarios through continuous efforts.  
Conceptualization, H.W. and P.Z.; methodology, P.Z.; software, M.Y.; validation, P.Z.; formal analysis, X.Y.; investigation, M.Y.; resources, H.W.; data curation, X.Y.; writing--original draft preparation, P.Z.; writing--review and editing, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a key project funded by the Hubei Provincial Department of Education, China, under grant number D20201601.  
**Institutional Review Board Statement:** Not applicable.  
Figure 14: Comparison chart of detection effect. (**a**) YOLOXs detection results; (**b**) Detection results of YOLOXs after improvement.