# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 4 Analysis and Discussion of Experimental Results
### Image Segmentation Results  
To evaluate the segmentation performance of the ADD model, this study compared its results with those of various methods, as shown in Table 4 and Figure 12.  
In Table 4, considering mIoU alone, DeepLabV3+ does not emerge as the best model. However, after considering the overall impact on the prediction results using mPA, we opted to improve this model. Unet, with its classic encoder-decoder structure, achieves the highest mIoU of 93.04% on the dataset. Nevertheless, its mPA is relatively low, indicating a higher number of misclassified pixels. As shown in Figure 12e, almost every predicted image from Unet exhibits missing boundaries, particularly in areas with strong lighting shadows. The unsophisticated upsampling and downsampling processes in Unet, coupled with the lack of global contextual information in the convolution operations, may result in the loss of features [34], causing the smaller predicted boundaries. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mIoU/\%** & **mPA/\%** & **FPS** & **Parameter/M** \\ \hline PSPNet & 91.74 & 97.22 & 20.76 & 49.13 \\ SegNet & 92.25 & 96.69 & 24.28 & 57.83 \\ Unet & 93.04 & 95.61 & 23.07 & 43.98 \\ DeepLabV3+\(\sim\)MobileNetV3 & 82.83 & 98.73 & 49.39 & 6.10 \\ DeepLabV3+\(\sim\)ResNet50 & 91.59 & 99.10 & 29.98 & 40.92 \\ ADD & 95.07 & 99.16 & 27.62 & 45.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluating indicators comparison of segmentation models. Figure 11: Results of YoloV5s. (**a**)Training curves; (**b**) detection results. Although PSPNet and SegNet have slightly lower mIoU scores compared to UNet, their higher mPA scores indicate more accurate prediction of region areas. However, both models have noticeable drawbacks. As depicted in Figure 12c,d, when the segmentation results are relatively complete, PSPNet tends to produce less smooth edges, particularly at the vertices. The spatial pyramid pooling structure in PSPNet integrates features from different scales, but the varying semantic information across scales affects the perception of edges, leading to some features being suppressed and compromising edge smoothness. SegNet performs slightly better in terms of prediction results, but almost all images exhibit minor jaggedness along the edges. This is due to the lack of effective contextual information capture modules during the encoding phase, reducing the model's segmentation capability for edge regions. When prominent lighting shadows are present, the aforementioned models mainly classify pixels in well-illuminated areas accurately. From Table 4 and Figure 12g, it is evident that the DeepLabV3+-ResNet50 model exhibits excellent overall performance metrics, segmentation results and FPS. This model achieves a remarkable mPA of 99.10%, indicating high accuracy in pixel classification and minimal missegmentation, making it well suited for segmenting hole images. However, DeepLabV3+ also has certain drawbacks. In certain images, similar to PSPNet's segmentation results, distortion at the corner points can occur. Nevertheless, this phenomenon is somewhat alleviated due to the adoption of atrous convolution in DeepLabV3+, which offers a larger receptive field and richer semantic information. However, even with these improvements, discontinuities in connected regions still occur in images with strong lighting shadows. Additionally, we employed the lightweight MobileNetV3 as the backbone network. Unfortunately, despite boasting the best real-time performance, this approach yielded poor results across various aspects except for the high mPA, as depicted in Figure 12f. The ADD model builds upon the DeepLabV3+ model by introducing the ECA mechanism and DASPP module. The improved algorithm incorporates attention mechanisms  
Figure 12: Performance comparison of models. (**a**) Original images; (**b**) GT; (**c**) PSPNet; (**d**) SegNet; (**e**) Unet; (**f**) DeepLabV3+-MobileNetV3+; (**g**) DeepLabV3+ ResNet50; (**h**) ADD.