# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 6 Conclusions  
This paper proposed an enhanced YOLOv8-based marine bentnos detection model, termed WDS-YOLO. The proposed model incorporates an advanced backbone network utilizing WTConv to significantly improve feature extraction capability when detecting marine bentnos objects in complex underwater environments. A novel DASPPF module was developed by integrating a deformable attention mechanism, which enables adaptive adjustment of the attention domain in response to input variations, thus minimizing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **P/\%** & **R/\%** & **mAP@50/\%** & **mAP@50:95/\%** & **FLOPs/G** \\ \hline P2 & 81.1 & 77.6 & 83.6 & 49.2 & 12.2 \\ SF-PAFPN & 83.2 & 78.1 & 84.7 & 49.5 & 11.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of small object detection methods.  
the influence of irrelevant data and improving detection accuracy. Furthermore, the SF-PAFPN module was implemented to facilitate the fusion of shallow feature information, thereby preventing the loss of small object details and improving small object detection accuracy, which consequently reduces both missed detection and false detection rates. The experimental results demonstrate that the proposed architecture, with 3.2 million parameters and 11.4 G FLOPs, achieves 85.6% mAP@50 on marine benthos detection, representing a 2.1 percentage absolute improvement over the baseline YOLOv8n (83.5%) while maintaining competitive efficiency and outperforming recent models, including YOLOv10n (82.8%) and YOLOv9-t (83.9%), thereby confirming its effectiveness in accurately detecting marine benthos objects in challenging environments and offering an innovative solution for automated underwater detection and robotic fishing.  
Although the SF-PAFPN structure effectively utilizes shallow feature map information to boost small object detection capability and requires less computation than implementing an additional small object detection layer, its computational overhead relative to the baseline model has increased. Therefore, significant opportunities exist for further algorithm optimization. Accordingly, future research will focus on maintaining detection accuracy while minimizing computational requirements, thus improving the model's detection speed. Additionally, considering the rapid advancements in object detection research, future work will explore state-of-the-art algorithms to further enhance the model's performance.  
Conceptualization, M.C.; methodology, J.Q.; software, J.Q.; validation, M.C. and J.Q.; formal analysis, M.C. and J.Q.; resources, M.C.; data curation, M.C. and J.Q.; writing--original draft preparation, J.Q.; writing--review and editing, M.C. and J.Q.; supervision, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research and Development Planning in Key Areas of Guangdong Province, NO. 2021B0202070001.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
R-CNN Region with CNN Features  
RT-DETR Real-Time Detection Transformer  
YOLO You Only Look Once  
NMS Non-Maximum Suppression  
FPN Feature Pyramid Network  
DCP Deformable Convolution Pyramid  
DASPPF Deformable Attention-based Spatial Pyramid Pooling Fast  
SE Squeeze-and-Excitation  
CA Coordinate Attention  
ECA Efficient Channel Attention  
CBAM Convolutional Block Attention Module  
EMA Efficient Multi-Scale Attention  
BRA Bilinear Routing Attention  
WTConv Wavelet Convolution  
URPC Underwater Robot Professional Contest