# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 5 Conclusions and Discussion  
In this study, the YoloV5s model was employed to extract the lifting hole regions from panoramic photos. Subsequently, the ADD model was utilized for pixel-level segmentation of the target regions, followed by fitting the segmented areas using the VLFGM method to output the key point coordinates for alignment. Experimental results demonstrate that the proposed approach achieves higher accuracy in coordinate estimation, thereby providing assurance for spatial point extraction. The following conclusions are drawn from this research:  
1. The YoloV5s model is capable of identifying lifting holes in various environments, with accurate boundary predictions of anchor boxes, ensuring reliable coarse localization and laying the foundation for subsequent tasks. 2. The ADD model exhibits superior stability and precision compared to other commonly used models, achieving mIoU and mPA metrics of 95.07% and 99.16%, respectively. 3. Even in cases where the segmentation results are suboptimal, the VLFGM method still performs well in most fitting tasks. It exhibits less dependence on image segmentation results compared to other methods. Moreover, the method achieves higher accuracy, with vertex coordinate errors reaching 5.39 pixels in the presence of abnormal images, reducing the overall error by 30.00%. The centroid error is as low as 1.72 pixels, representing a reduction of approximately 28.93%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Centroid Error/Pixels**} \\ \hline
**No.** & **MBR** & **BP** & **CD** & **LS** & **VLFGM** \\ \hline
1 & 4.81 & 2.99 & 4.25 & 2.47 & 2.14 \\
2 & 1.60 & 1.48 & 1.03 & 2.02 & 1.12 \\
3 & 2.92 & 9.39 & 4.79 & 2.76 & 2.46 \\
4 & 3.26 & 1.85 & 2.06 & 1.25 & 1.03 \\
5 & 0.75 & 0.75 & 1.18 & 0.56 & 0.56 \\
6 & 4.07 & 4.12 & 2.96 & 2.30 & 1.68 \\
7 & 1.41 & 1.84 & 1.97 & 1.77 & 1.50 \\
8 & 3.75 & 5.48 & 3.86 & 5.25 & 1.35 \\
9 & 2.70 & 2.25 & 1.35 & 1.95 & 1.25 \\
10 & 5.02 & 4.35 & 7.22 & 3.88 & 4.07 \\ \hline Mean error & 2.96 & 3.45 & 3.07 & 2.42 & 1.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of centroid errors. Of course, our method also has some limitations. The current weight assignment in the VLFGM method is predefined. However, weight acquisition in this method can also be enhanced through Gaussian distributions and machine learning, enabling better adaptive performance. Since we utilized Python programming, when the number of sample points is within 100, VLFGM might require approximately 0.05 s, involving the efficiency of the programming language and the code. Furthermore, construction operations prioritize detection speed and accuracy. Due to limitations in image resolution, and hardware capabilities, our method can only achieve a frame rate of 9.4 FPS. To enhance the real-time capability of the localization method, two approaches can be considered. First, increasing model prediction speed, including object detection and image segmentation, can be pursued by adopting a smaller backbone with a precise trade-off between accuracy and speed. Second, given the frame rate constraints of industrial cameras, employing faster frame rate image capture devices (which may involve lowering resolution, accuracy, or increased investment) constitutes another limitation of our approach. In practical operational scenarios, suspensions are often difficult to keep still. If noticeable vibrations occur, captured images by the device may exhibit blurring or other interferences, which is another aspect to consider. Obtaining accurate coordinate information for key point extraction forms the foundation of visual alignment and is crucial for visual guidance. Future work will primarily focus on enhancing the usability of semantic segmentation models on target images, particularly improving the restoration of boundaries and vertices. Target region fitting can only serve as a mitigating approach. At the same time, visual localization has become an important direction in intelligent construction.