# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 2 Methods
### The YoloV5s Model for Hole Detection  
Although the Yolo series has been updated to version 8, YoloV5 [23] is still capable of handling many detection tasks. Additionally, in this scenario, the network model needs to be deployed on a hoisting device. In relation to existing models, we conducted several comparisons as shown in Table 1.  
From Table 1, it is evident that YoloV5s outperforms EfficientDet in terms of both accuracy and FPS prediction. Despite the notable speed advantage of YoloV7-tiny, this model incurs a substantial graphics memory overhead during training, approximately 9.8 GB. In the context of the same dataset, training the YoloV7-tiny model demands three to four times the temporal investment required by the YoloV5s model, thereby implying escalated resource allocation for model training and refinement. While YoloXs marginally surpasses YoloV5s in mAP0.50:0.95, its heightened complexity engenders a processing lag of 11 frames in contrast to YoloV5s. Notably, YoloV8s fails to exhibit a significant ascension in accuracy. Furthermore, the YoloV8s boasts a larger parameter space, mandating augmented computational resources. Upon comprehensive juxtaposition of these outcomes, the YoloV5s model is elected as the preferred candidate, owing to its parsimonious parameter configuration and a judicious equilibrium between processing velocity and precision.  
The YoloV5s feature extraction network consists of the Backbone, Neck, and Head components, as shown in Figure 2. First, the Mosaic data augmentation is applied within the Backbone, which can adaptively scale and crop the input images. The Focus module transforms high-resolution images into multiple low-resolution feature maps, capturing different-sized feature information. Additionally, the CSP (cross-stage partial network) structure enables local feature connections across stages, effectively reducing computation and parameters. The SPP (spatial pyramid pooling) structure captures semantic information at different scales, facilitating multi-scale fusion and improving the network's ability to detect objects of different sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP0.50:0.95\%** & **GFLOPs** & **Weight/M** & **FPS** \\ \hline EfficientDet-b1 & 78.76 & 14.3 & 25.6 & 23.3 \\ YoloV5s & 87.60 & 15.9 & 17.1 & 54.9 \\ YoloXs & 87.72 & 26.8 & 34.3 & 43.1 \\ YoloV7-tiny & 86.24 & 13.2 & 14.4 & 91.0 \\ YoloV8s & 87.81 & 24.3 & 22.5 & 72.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different detection models on our dataset.  
Figure 1: Flow chart of this study.  
The Neck layer consists of the FPN (feature pyramid network) and PAN (path aggregation network). The FPN layer utilizes bottom-up upsampling to propagate and fuse deep semantic features. Then, through top-down lateral connections, the bottom-level features are fused with the top-level features to form a multi-scale feature pyramid [24]. The PAN layer aggregates features layer by layer from shallow layers, and each feature map is fused with a high-resolution shallow feature map to preserve detailed features [25]. The Head layer predicts the location features and class information of the hosting holes from the feature maps outputted by the Neck layer.