# Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images
## 3 Experiments
### Ablation Experiment in Different Datasets  
To further illustrate the advantages of our proposed ENAM attention network module in object detection and segmentation, we performed validation evaluations on the COCO128-seg datasets. Meanwhile, we replaced three ENAM modules in the backbone network architecture with CBAM [24], SE [25], CA [23], RFA [22], DCA, YOLOV11, ENCM (Ours), and ENAM (ours). The final evaluation results are shown in Table 3.  
DCA is the combination of significance and coordinate attention, and RFDA is the combination of significance and receptive-filed attention. It is obvious from Table 3 that compared to SE, CA, RFA, and RFCA, our ENAM performs slightly better than them. On the COCO128 dataset, the MAP@50 of the V8n + SE model is 80.8%, while the MAP@50 of the V8n + ENAM model is increased to 87.4%. This shows that the ENAM attention module is more effective than the SE module in improving the accuracy of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weight (M)** & **pmask** & **MAP\({}^{\textit{mask}}_{\textbf{50}}\)** & **MAP\({}^{\textit{mask}}_{\textbf{50.95}}\)** \\ \hline V8n-SEG & 6.8 & 88.6 & 84.1 & 44.2 \\ SE & 6.8 & 87.1 & 87.0 & 46.2\({}_{+2.0}\) \\ CBAM & 6.9 & 90.0 & 85.0 & 46.9\({}_{+2.7}\) \\ CA & 8.4 & 88.2 & 83.6 & 45.4\({}_{+1.2}\) \\ EMA & 6.8 & 86.7 & 83.9 & 43.9\({}_{-0.3}\) \\ RFA & 8.5 & 90.9 & 89.0 & 47.1\({}_{+2.9}\) \\ ENCM (ours) & 8.6 & 90.8 & 89.5 & 49.3\({}_{+5.1}\) \\ Gelan-CSEG(V9) & 53.1 & 90.0 & 89.9 & 45.9\({}_{+1.7}\) \\ Gelan-C-DSEG(V9) & 55.8 & 87.9 & 88.2 & 46.5\({}_{+2.3}\) \\ ENAM (ours) & 7.0 & 96.0 & 90.9 & 47.9\({}_{+3.7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the other attention modules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Weight (M)** & **Flops (G)** & **MAP\({}^{\textit{box}}_{\textbf{50}}\) (\%)** & **MAP\({}^{\textit{box}}_{\textbf{50}}\) (\%)** & **MAP\({}^{\textit{mask}}_{\textbf{50}}\) (\%)** & **MAP\({}^{\textit{mask}}_{\textbf{50}}\) (\%)** \\ \hline V8n + SE & 7.3 & 12.6 & 80.8 & 62.6 & 71.3 & 42.2 \\ V8n + CA & 7.3 & 12.7 & 80.7 & 62.4 & 70.5 & 41.9 \\ V8n + CBAM & 7.4 & 12.7 & 84.5 & 66.2 & 75.3 & 47.2 \\ V8n + EMA & 7.3 & 12.6 & 76.4 & 58.5 & 65.7 & 37.3 \\ V8n + RFA & 9.5 & 15.3 & 85.0 & 70.8 & 76.7 & 49.7 \\ V8n + DCA & 8.9 & 15.0 & 83.0 & 64.4 & 73.2 & 44.4 \\ V8n + RFCA & 10.7 & 26.5 & 86.2 & 72.2 & 79.8 & 53.3 \\ V8n + ENAM & 10.9 & 27.1 & 87.4 & **73.9** & 82.1 & **56.5** \\ V8s + ENAM & 37.0 & 99.8 & 91.0 & 79.9 & 87.7 & 66.5 \\ V8m + ENAM & 74.3 & 219.9 & 94.3 & 85.6 & 92.6 & 74.6 \\ V8l + ENAM & 114.4 & 391.5 & 94.8 & 87.2 & 92.4 & 76.6 \\ V8x + ENAM & 178.2 & 611.1 & 95.1 & 87.8 & 93.9 & 77.9 \\ V11n & 6.7 & 10.4 & 85.3 & 71.0 & 79.6 & 54.2 \\ +RFA & 6.5 & 10.5 & 91.9 & 78.1\({}_{+7.1}\) & 89.0 & 66.1\({}_{+11.9}\) \\ +RPCA & 6.6 & 10.6 & 94.1 & 83.0\({}_{+12.0}\) & 91.6 & 72.1\({}_{+17.9}\) \\ +RFD & 7.1 & 20.1 & 93.3 & 81.5\({}_{+10.5}\) & 90.1 & 68.5\({}_{+14.3}\) \\ +ENCM & 6.6 & 10.6 & 94.0 & 83.4\({}_{+12.4}\) & 91.4 & 71.0\({}_{+16.8}\) \\ +ENAM & 7.3 & 20.2 & 93.8 & 84.0\({}_{+13.0}\) & 91.0 & 71.0\({}_{+16.8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results with different attention modules of Coco128-Seg datasets. The attention module had various effects on model performance. For example, the performance of the V8n + ENAM model on the MAP@50:95 index (73.9%) was significantly better than that of the V8n + SE model (62.6%), indicating that the ENAM module performs better when dealing with more stringent evaluation criteria. As the size of the model increases, the performance usually improves. From V8 n to V8 x, with the increase of model weight and calculation amount, the MAP index also increases accordingly. For example, the V8x + ENAM model performs highest on all listed metrics. This article adopted the SKU110k dataset to further verify the effectiveness of object detection.