# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 4 Experiments
### Experiment Results
#### 4.2.5 Sub-Experiments of Improvement Points  
* Sub-experiment 1: Comparison of various attention modules  
To assess the effectiveness and feasibility of enhancing the SPPF with Deformable Attention (DA) and evaluate its performance in multi-scale feature fusion, we integrated various attention mechanisms, including SimAM, SE, CBAM, CA, EMA, ECA, and BRA, into the SPPF module. These modules were incorporated at the identical position in the model. The relevant findings are summarized in Table 5.  
As shown in Table 5, the enhancement of the SPPF module using deformable attention (DA) resulted in the most significant improvement in the model's overall performance. The precision (P), mAP@50, and mAP@50:95 metrics all outperformed those of other attention modules. This can be attributed to DA's ability to adaptively adjust the attention domain, enabling more effective representation of the global features of marine bentnos objects in complex underwater environments. Therefore, this attention mechanism is particularly suitable for marine bentnos detection tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Module** & **P/\%** & **R/\%** & **mAP@50/\%** & **mAP@50:95/\%** & **Params/M** & **FLOPs/G** \\ \hline SPPF & 81.5 & 77.8 & 83.5 & 48.9 & 3.0 & 8.1 \\ SPPF + SimAM & 82.4 & 76.4 & 83.6 & 48.8 & 3.0 & 8.1 \\ SPPF + SE & 82.1 & 75.8 & 83.3 & 48.5 & 3.0 & 8.1 \\ SPPF + CBAM & 83.4 & 75.7 & 83.5 & 48.8 & 3.0 & 8.1 \\ SPPF + CA & 82.5 & 76.4 & 83.4 & 48.8 & 3.0 & 8.1 \\ SPPF + EMA & 82.4 & 76.4 & 83.6 & 48.8 & 3.0 & 8.1 \\ SPPF + BCA & 80.4 & 77.9 & 83.9 & 48.9 & 3.0 & 8.1 \\ SPPF + BRA & 80.7 & 77.8 & 83.6 & 48.8 & 3.2 & 8.3 \\ SPPF + DA & 83.6 & 77.2 & 84.3 & 49.4 & 3.2 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of SPPF enhanced with various attention modules.  
Figure 9: Examples of WDS-YOLO errors: (**a**) Blurriness caused the rock to be recognized as an object; (**b**) The shadow was mistaken for a echinus; (**c**) The holdhurian was missed due to objects blocking each other. Green bounding boxes indicate correctly detected objects, red boxes denote false positives, and blue boxes represent false negatives.  
* Sub-experiment 2: Comparison of small object detection methods  
To assess the effectiveness of the proposed SF-PAFPN neck structure for small-object marine bentnos detection and its superiority over the current mainstream approach of adding a small-object detection layer (P2), we conducted comparative experiments based on YOLOv8n. The experimental results are presented in Table 6.  
As evident from Table 6, the proposed SF-PAFPN structure demonstrates improvements in precision (P), recall (R), mAP@50, and mAP@50:95 metrics compared to the addition of the small-object detection layer P2. This suggests that the SF-PAFPN structure exhibits enhanced feature fusion capabilities for small-object marine bentnos, resulting in superior detection performance. Furthermore, the SF-PAFPN structure requires less computational resources than the added detection layer P2, further validating its superiority.