# McAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
## 4 Experimental Results and Analyses
### Comparative Experiments  
In order to validate the rationality of the designed Light-BiFPN as a feature fusion mechanism, this section compared the Light-BiFPN with relatively mainstream lightweight networks [27; 28; 29; 30; 31; 32; 33] as replacements for the backbone network. The results are shown in Table 2.  
The data in Table 3 showed that, compared to the base model, Light-BiFPN-YOLOv5n outperformed the base model in all aspects. Among the other lightweight networks, ShuffleNetv2 [30] possessed the fewest parameters and least computational complexity. However, it incurred significant accuracy loss, exhibiting the poorest performance in the comparative experiments. When compared to Light-BiFPN, its mAP value differed by 14.4%. On the other hand, although PPLCNet [31] demonstrated as fast a detection speed as the backbone network, its detection accuracy was relatively low, which did not guarantee satisfactory detection outcomes. RepghostNet [33] performed as the best network among the contrasted lightweight backbone networks regarding detection performance. However, compared to Light-BiFPN, its mAP value differed by 1.8%. Additionally, RepghostNet [30] exhibited disadvantages regarding parameter count, computational complexity, and detection speed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Model** & **Parameters/M** & **GFLOPs** & **[EMAIL_ADDRESS]/\%** & **FPS** \\ \hline
1 & Baseline(YOLOv5n) & 1.77 & 4.3 & 68.8 & 158 \\
2 & Baseline + GSConv & 1.23 & 3.1 & 67 & 163 \\
3 & Baseline + GSConv + PGSC3 & 1.22 & 3.1 & 68.9 & 166 \\
4 & Baseline + Light-BiFPN & 1.25 & 3.3 & 69.4 & 161 \\
5 & Baseline + MobilViT3 & 1.79 & 4.4 & 69.9 & 149 \\
6 & Baseline + Wise-IoU & 1.77 & 4.3 & 69.8 & 155 \\
7 & Baseline + Light-BiFPN + MobilViT3 & 1.3 & 3.4 & 70.3 & 152 \\
8 & Baseline + Light-BiFPN + MobilViT3 + NAM & 1.3 & 3.4 & 71.4 & 147 \\
9 & Baseline + Light-BiFPN + MobilViT3 + NAM + & 1.3 & 3.4 & 71.9 & 142 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of ablation experiments with different modified methods. Light-BiFPN boasted the highest detection accuracy compared to other lightweight networks, with detection speed only falling behind ShuffleNetv2 [30]. As evident in Table 3, utilizing the Light-BiFPN feature fusion structure achieved a favorable balance among detection accuracy, model parameters, and detection speed. This indicated that the designed Light-BiFPN structure was reasonably suitable as a fusion network. In order to validate the effectiveness of using Wise-IoU, a comparison was conducted with other loss functions [34; 35; 36] on the YOLOv5n model. As seen from Table 4, employing the Wise-IoU loss function holds a notable advantage in enhancing accuracy compared to other loss functions. Compared to the base model, only a marginal sacrifice in detection speed results in a 1% increase in the model's mAP value. Figure 8: Comparison of detection results between YOLOv5n and MCAW-YOLO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters/M** & **GFLOPs** & **[EMAIL_ADDRESS]/\%** & **FPS** \\ \hline GhostNet-YOLOv5n [27] & 1.18 & 2.9 & 66.7 & 140 \\ MobileNetv3-YOLOv5n [28] & 1.3 & 2.2 & 61.4 & 120 \\ EfficientNet-YOLOv5n [29] & 0.94 & 2.2 & 64.3 & 126 \\ ShuffleNet-YOLOv5n [30] & 0.6 & 1.3 & 55 & 158 \\ PPLCNet-YOLOv5n [31] & 0.76 & 1.6 & 60 & 172 \\ MobileNet-YOLOv5n [32] & 1.14 & 2.4 & 59.4 & 147 \\ RepghostNet-YOLOv5n [33] & 1.53 & 3.6 & 67.6 & 147 \\ Light-BiFPN-YOLOv5n & 1.28 & 3.3 & 69.4 & 161 \\ YOLOv5n & 1.77 & 4.3 & 68.8 & 158 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparative experiments of Light-BiFPN and different lightweight models. To further validate the effectiveness of the proposed improvement algorithm, this section compared the improved algorithm with other YOLO series algorithms.