## 3 Pressure Relief Hole Identification Model Based on Improved Faster R-CNN
### Pressure Relief Hole Identification Model Based on Improved Faster R-CNN
#### 3.3.2 Top-Down Multi-Layer Feature Fusion  
According to image analysis of the pressure relief holes, most of the pressure relief holes are 20 \(\times\) 20-40 \(\times\) 40 pixels. The SqueezeNet model will conduct the downsampling of the image with an amplitude value of 16 and repeat it 4 times. Then, the size of the feature region mapped by the relief hole is 1 \(\times\) 1-2 \(\times\) 2 on the last convolution layer. Finally, after the pooling layer, the region size is magnified several times, resulting in blurred image pixels, which may eventually lead to missed recognition of the pressure relief hole object in the image.  
The shallow feature map in CNN can extract the shape, color, and texture of the object well, while the more general semantic information requires the deep feature map to for extraction. In CNN, with the increase in the number of network layers, the resolution of the feature map will also decrease, which is caused by the downsampling and pooling operations. In view of this, many scholars are studying how to achieve the detection of small-sized objects, and the current method is mainly to fuse the shallow and deep features. In addition, in the convolutional neural network, the feature maps of adjacent layers are highly correlated, and the performance improvement of the model is not obvious after their fusion. Using the above research ideas for reference, this paper designs a multi-layer feature fusion method which fuses the shallow and deep information from top to bottom. After upsampling, the feature map after the last downsampling is connected with the feature maps after the previous several iterations of downsampling. The final output results are used for related operations in the candidate area of the relief hole target.  
The specific processes are as follows:  
1. Firstly, the feature map passing through fire12 is enlarged to be equal to the feature map of fire10. Then, the enlarged feature map is connected in series with the feature map of fire10, and performs the BN normalization. Finally, the output channel number is reduced to 512.
2. The obtained feature map is upsampled again, and the enlarged size is consistent with the size of the feature map of fire6. Then, the upsampled feature map is connected in series with the feature map of fire6, and performs the BN normalization. Finally, the output channel number is also changed to 512.
3. The output feature map is performed by the maximum pooling operation, and then sent to the RPN candidate region selection network and the Faster R-CNN relief hole identification network, respectively, to achieve accurate identification of the relief holes.  
The main reason why the maximum pooling operation is performed in step (3) is that if the downsampling is performed twice, the size of the obtained feature map is 75 \(\times\) 75,  
Figure 6: Multi-scale image input of Faster R-CNN.  
which will take a lot of time to identify the relief hole on this image size. If the number of downsampling iterations is 3, the size of the feature map after downsampling is only 37 \(\times\) 37. The feature map size is greatly reduced, which is more suitable for hole target extraction and will consume less time for calculation.  
Based on the improved SqueezeNet lightweight convolution neural network, through integrating the strategies of multi-scale image input and top-down multi-layer feature fusion, this paper designs an improved Faster R-CNN model for pressure relief hole target recognition, as shown in Figure 7.