# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 4 Analysis and Discussion of Experimental Results
### Image Segmentation Results
at multiple stages, resulting in more reasonable feature weight allocation and a better focus on relevant features. Moreover, the model's parameter count increases only slightly, approximately 4.23 M. The densely connected multi-scale feature fusion module offers a larger receptive field and stronger semantic information extraction capability compared to the original model. As a result, ADD achieves the highest mIoU and mPA scores, as shown in Table 4. Simultaneously, the proposed model exhibits the best segmentation results across seven different environments, as illustrated in Figure 12h. In the first group of images, ADD's predictions along the right edge do not exhibit downward bending, particularly noticeable in the lower right corner. Conversely, scenarios (d-g) manifest such curvatures, although these might seem present in the original images due to imaging effects. In the second and fifth image groups, both ADD and scenarios (d-g) display jagged edges along the left and right boundaries. Within the fourth image group, ADD's superiority over other models is distinctly pronounced. In the sixth experimental set, only ADD and the baseline show favorable outcomes. However, the baseline's classification outcome in the upper left corner appears rounded and lacks sharpness, akin to an obtuse angle, which indicates evident distortion. Although some resulting images may exhibit unevenness, overall, the ADD model produces the smoothest edge effects. Furthermore, the model demonstrates superior resistance to lighting interference, preventing the disconnection of connected regions, which is crucial. The ADD provides a guarantee for subsequent detection tasks. Moreover, we have conducted an analysis of the contributions of each module to the overall model, and the results are presented in the following Table 5. mIoU and mPA are employed as evaluation metrics. The DASPP connects features across different scales. As evident from Table 5, the introduction of DASPP leads to a 0.36% enhancement in the model's mIoU metric. The incorporation of the ECA mechanism in low-level features results in a performance gain of \(1.17\%\), signifying the retention of significant original information in the shallow network layers and emphasizing the favorable role of attending to these features. The introduction of attention mechanisms at the output end of the backbone network (referred to as the deep level in the table) optimizes both mIoU and mPA by 2.18% and 0.08%, respectively, underscoring the importance of deep-level semantic information for model performance, despite its susceptibility to loss and thus meriting profound consideration. Comparing experiments in groups 6 and 8, it is evident that introducing the ECA mechanism in ResNet effectively mitigates the impacts of operations such as pooling. Even with an already optimized foundation, the model's mIoU metric can be further augmented by 1.01%, effectively counteracting excessive information loss caused by the backbone network's pooling layers. By contrasting experiments in groups 4, 6, and 8, it is discernible that introducing attention mechanisms both before and after the backbone network's output end can preserve essential feature information. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Baseline** & **DASPP** & **Low Level ECA** & **Deep Level ECA** & **ResNet ECA** & **mIoU/\%** & **mPA/\%** \\ \hline
1 & \(\surd\) & & & & 91.59 & 99.10 \\
2 & \(\surd\) & \(\surd\) & & & 91.95 & 99.12 \\
3 & \(\surd\) & & \(\surd\) & & 92.76 & 99.10 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & & 93.30 & 99.14 \\
5 & \(\surd\) & & \(\surd\) & & 93.77 & 99.18 \\
6 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 94.06 & 99.12 \\
7 & \(\surd\) & & & \(\surd\) & 92.14 & 99.08 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 95.07 & 99.16 \\ \hline \end{tabular}
\end{table} 
Table 5: Evaluating indicators comparison of segmentation models.