# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
## 4 Results and Discussion
### Comparison of Experimental Results
To validate the performance improvement and speed enhancement of the model after incorporating the Ghost module and an improved ECA mechanism, ablation experiments were conducted in this study. A comparison was made with models proposed in datasets from various industrial domains. The results are shown in the Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Classification Accuracy** & **Average Runtime per Image** \\ \hline CNN & 71.25\% & 19.98 ms \\ GhostCNN & 77.5\% & 10.07 ms \\ CNN+PP [26] & 66.2\% & 87 ms \\ ECACNN & 88.75\% & 21.30 ms \\ Unet [25] & 90.21\% & 166.7 ms \\ deepCrack [25] & 93.15\% & 141 ms \\ MCuePushU [24] & 98.52\% & 549 ms \\ ECAGhostCNN & 93.75\% & 10.53 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments with rapid classification methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module Name** & **Model Size** & **MAdd** & **FLOPs** & **MemR + W(B)** \\ \hline GhostModule 1 & 76 & 39,845,888.00 & 20,971,520.00 & 24,117,552 \\ ECABlock 1 & 1 & 0.00 & 0.00 & 0 \\ MaxPool 1 & 0 & 786,432.00 & 1,048,576.00 & 5,242,880 \\ GhostModule 2 & 452 & 59,244,544.00 & 30,146,560.00 & 12,584,720 \\ ECABlock 2 & 3 & 0.00 & 0.00 & 0 \\ MaxPool 2 & 0 & 393,216.00 & 524,288.00 & 2,621,440 \\ GhostModule 3 & 1704 & 55,836,672.00 & 28,180,480.00 & 6,298,272 \\ ECABlock 3 & 3 & 0.00 & 0.00 & 0 \\ MaxPool 3 & 0 & 196,608.00 & 262,144.00 & 1,310,720 \\ GhostModule 4 & 6608 & 54,132,736.00 & 27,197,440.00 & 3,172,160 \\ ECABlock 4 & 3 & 0.00 & 0.00 & 0 \\ MaxPool 4 & 0 & 98,304.00 & 131,072.00 & 655,360 \\ FC1 & 2,097,216 & 4,194,240.00 & 2,097,152.00 & 8,520,192 \\ FC2 & 2080 & 4064.00 & 2048.00 & 8704 \\ FC3 & 33 & 63.00 & 32.00 & 264 \\ Total & 2,108,179 & 214.73 M & 110.56 M & 61.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ECAGhostCNN Model complexity. From the experimental results, it is evident that the ECACNN model achieved a 17.50% increase in accuracy compared to the CNN model, albeit with a decrease in runtime speed. The MCuePushU model achieved an accuracy as high as 98.52%, but with a runtime speed of 549 ms. Meanwhile, both the deepCrack and Unet models exhibited significantly slower runtime speeds compared to the ECAGhostCNN model's 10.53 ms. This may be attributed to the fact that MCuePushU and deepCrack are extensions of Unet, which have a large number of parameters that require more computation time. The GhostCNN model demonstrated a speed improvement of 9.91 ms over the CNN model, with little change in classification accuracy. The results indicate that the ECA module can enhance the model's focus on semantic information in the images, thereby improving the model's accuracy. The Ghost module can reduce parameter calculations, improve operational efficiency, and facilitate lightweight deployment of the model. The attentional Ghost module proposed in this paper has a significant optimization effect on the CNN model. It can achieve high accuracy while building lightweight models, enhancing model runtime speed, and facilitating model usage and deployment.