## 3 DAMP-YOLO Network
### Network Pruning  
The model trained by the neural network contains some weight parameters with a small proportion of weight and only plays a minor role during inference. Although these parameters have little influence on the calculation results, they require much calculation. Deploying models on mobile devices with limited memory and computing resources requires a model compression method that only sacrifices a little accuracy and removes these trivial weight parameters. Network pruning is a mainstream model compression method. By pruning unimportant neurons, filters, or channels, we can effectively compress the parameters and the amount of calculation the model undertakes.  
In this paper, we use the network slimming [43] method to implement the channel pruning algorithm based on the BatchNorm layer, and the method uses the parameters of the BN layer as the evaluation-channel-importance index. The calculation formula of the BN layer is as follows:  
\[\hat{z}=\frac{Z_{in}-\mu_{\beta}}{\sqrt{\sigma_{\beta}^{2}+\epsilon}} \tag{4}\]  
\[Z_{out}=\gamma\hat{z}+\beta \tag{5}\]  
Figure 9: The example images of meter data augmentation: (**a**) the Raw image represents the unprocessed original image; (**b**) the Channel Multiply image represents the multiplication of each channel of the image to adjust the brightness and darkness; (**c**) the Gaussian Blur image denotes the smoothing of the image to reduce noise and reduce details; (**d**) the Gaussian Noise image represents the addition of noise to the image so that the model can learn to remove noise interference; (**e**) the Alpha Grid image represents adding a translucent black and white mask to the image to simulate realistic shadow occlusion; the (**f**) Gray Scale image represents the conversion of three-channel images into single-channel grayscale images to further eliminate color interference.  
In Formulas (4) and (5), \(\gamma\) and \(\beta\) are the normalization parameters of the BN layer, \(Z_{in},Z_{out}\) represent the input and output of the BN layer, respectively, \(\mu_{\beta},\sigma_{\beta}\) represent the mean and variance of the BN layer, and \(\epsilon\) represents a small constant to prevent the denominator being 0. In Formula (5), when \(\gamma\) is close to 0, the output has nothing to do with the input. Therefore, the channel pruning algorithm uses the \(\gamma\) parameter of the BN layer as the scaling factor of network pruning to measure the importance of each channel, multiplies \(\gamma\) with the output of each channel, and then jointly trains the network weight of the original network and \(\gamma\) scaling factor; then, it counts and sorts \(\gamma\), directly removing the input-output relationship of this part of the channel, which is less than the setting global threshold, to compress the parameter amount and calculation amount undertaken by the model.  
Figure 10 shows the overall scheme of model channel pruning. In the pruning process, we first need to perform sparse training on the pretrained model, update the gradient during backpropagation, then prune the noncritical channels in the model by setting the pruning rate, and finally retrain the pruned model to obtain the final model.