# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 5 Results and Discussion  
### Comparisons with State-of-the-Art Methods  
In this study, the proposed architecture is compared with state-of-the-art models used for semantic segmentation purposes. The predictions of the proposed segmentation structure are randomly selected from the test set and compared with advanced models such as FCN, U-Net, Deeplab v3+, U-Net++, AttentionU-Net, and Trans U-Net, as shown in Figure 7. From the figure, it is evident that the proposed model can most closely segment these defect areas compared to other segmentation methods. The most challenging areas to segment are Fray and its connection with Break. Therefore, most segmentation models perform poorly in segmenting Break and Fray, while our proposed segmentation network satisfactorily segments these regions. From Table 2, it is evident that the proposed segmentation network in this paper outperforms other semantic segmentation algorithms, having the highest mIoU, as well as the lowest Flops and lower Params. U-Net++, which combines DenseNet-like structures with dense skip connections to improve the gradient flow, slightly enhances the mIoU but increases the number of Flops and Params significantly, making it less suitable for real-time applications and low memory requirements. Attention U-Net introduces an attention mechanism into U-Net, improving the mIoU, but with increased Flops and Params. Trans U-Net, incorporating transformers, achieves the best segmentation accuracy among the other models but at the cost of higher Flops and Params, similar to U-Net++. Figure 7: Detection results comparison on the magnetic tile dataset. Our model achieved an efficient inference time of 6.25 milliseconds and high accuracy metrics, surpassing competitors like DeepLab v3+, U-Net++, and Trans U-Net in terms of performance. Although FCN is the fastest with an inference time of only 2.56 milliseconds, its accuracy is lower compared to DEU-Net. U-Net and its advanced versions, despite their moderate inference speeds, also do not match the combined speed and accuracy of DEU-Net. From Figure 8, it is visually clear that, although U-Net++, Trans U-Net, and Deeplab v3+ achieve good mIoU and F1 scores, the proposed segmentation network demonstrates advantages by achieving comparable mIoU and F1 scores at lower Flops and Params. Meanwhile, FCN, U-Net, and Attention U-Net have lower Flops and Params but poorer segmentation accuracy. To validate our segmentation model, we also used the feature-similar KSDD dataset for verification. The KSDD dataset contains 50 images of electronic commutator surfaces. These images capture minor damage or cracks on the plastic encapsulated surfaces of electronic commutators, collected under controlled conditions such as uniform lighting, and provide pixel-level annotations of defects. The dataset comprises a total of 399 images, of which 52 images contain defects, while 347 images are defect-free. Since the images were resized to the same dimensions as those in the magnetic tile dataset before being input into the network, the number of parameters and Flops are the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **FLOPs** \\ **(GMac)** \\ \end{tabular} & \begin{tabular}{c} **Inference Time** \\ **(ms)** \\ \end{tabular} & \begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} & \
\begin{tabular}{c} **mIoU** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **F1** \\ **(\%)** \\ \end{tabular} \\ \hline FCN & 39.842 & 2.56 & 18.644 & 61.37 & 70.16 \\ U-Net & 62.791 & 4.35 & 17.263 & 63.81 & 71.34 \\ Deeplab v3+ & 64.109 & 13.46 & 39.63 & 63.31 & 72.52 \\ U-Net++ & 312.615 & 16.03 & 47.189 & 64.38 & 72.05 \\ AttentionU-Net & 50.382 & 7.31 & 93.231 & 62.46 & 70.94 \\ Trans U-Net & 104.112 & 17.14 & 34.897 & 66.44 & 74.24 \\ DEU-Net (Segmentation) & 36.675 & 6.25 & 19.710 & 66.94 & 74.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Segmentation results comparison on the industrial magnetic tile defect dataset.