# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 2 Methods
### The ADD Network Model  
#### 2.3.1 Overall Structure  
Despite being introduced in 2018, DeepLabV3+ [10] continues to exhibit the most favorable performance on the PASCAL VOC 2012 test dataset. The model demonstrates superior comprehensive capabilities and exceptional scalability. These attributes encompass the utilization of dilated convolutions to enlarge the receptive field, depthwise separable convolutions to reduce model parameters, multi-scale feature fusion for extracting semantic information from diverse layers, and the amalgamation of shallow and deep-level information to balance original and high-level features. Consequently, ADD is a semantic segmentation model based on the improved DeepLabV3+. The main optimization modules include: using three ECA modules to enhance the focus on the features of interest, replacing the ASPP (atrous spatial pyramid pooling) module with DASPP (dense connection atrous spatial pyramid pooling), and adding a shallow feature branch. The network structure of ADD is shown in Figure 3.  
The proposed model still retains the encoder-decoder structure and uses ResNet50 as the backbone. In the encoding phase, the input image is first fed into the ResNet50-ECA residual network for feature extraction. The features outputted from the backbone network can be divided into two types: shallow semantic features of 256 channels from the 1 st layer and deep features from the last layer. The shallow features are directly passed to the decoder, while the latter undergoes ECA (Efficient channel attention) weight assignment and further feature extraction through the DASPP module. The DASPP module includes parallel feature extraction operations, including 1 \(\times\) 1 convolutions, dilated convolutions with dilation rates of 6, 12, and 18, and global average pooling, converting the 2048-channel backbone  
Figure 2: The architecture of the YoloV5s.  
network output into five 256-channel feature tensors. The DASPP module employs dense connections for each convolutional branch and these five differently processed feature tensors are concatenated to achieve multi-scale feature fusion. Finally, a \(1\times 1\) convolution is applied to convert the number of feature channels to 256, obtaining the high-level feature.  
In the feature decoding phase, the input features consist of the original low-level feature 1, the ECA-processed low-level feature 2, and the high-level feature. The addition of the new low-level feature 2 has almost no impact on the network's parameter count and provides better weight distribution for the shallow features, resulting in better preservation of the original information. Both shallow features undergo \(1\times 1\) convolution for feature dimension reduction. The deep semantic features are first upsampled by a factor of 4, reshaping the feature map to 1/4 of the original size. Then, the three feature tensors are fused together by addition, resulting in a 256-channel feature map. The fused features need further refinement, so a \(3\times 3\) convolution is applied to refine the feature map, changing its channel number and avoiding the occurrence of aliasing effects between the original features and the upsampled features [26; 27]. Finally, the convolved image is upsampled by 1/4 to restore it to the original size and undergo pixel-wise classification to obtain the predicted image.