# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### YOLOv4-MobileNetv3 Framework  
Through the analysis of the network structure of YOLOv4, it can be seen that using CSPDarkNet53 as the backbone extraction network can achieve better feature extraction capabilities, but the network model still has the problem of a large number of parameters and high computational complexity, which will consume a lot of computing resources and is not suitable for deployment to embedded devices and FPGAs. We propose the YOLOv4-MobileNetv3 network framework to solve the problem of large model parameters and high computational complexity. The YOLOv4-MobileNetv3 network framework is shown in  
Figure 4: GridMask-Mosaic data enhancement effect.  
Figure 5. By adopting the lightweight network MobileNetv3 as the backbone extraction network, the depthwise separable convolution is used to improve the network convolution layer to reduce the amount of parameters. MobileNetv3 is a lightweight network that, compared to other lightweight networks like Ghost, achieves higher performance while maintaining its lightweight nature through the introduction of effective design strategies and improved structure. It excels in accuracy and is particularly suitable for tasks that require higher precision. By using lightweight components and operations, MobileNetv3 effectively reduces the number of parameters and computational complexity, resulting in better efficiency in resource-constrained environments such as mobile devices. This means that it can run on lower computational resources and is well suited for embedded devices and mobile applications. In order to make the DPU support the neural network operator of the network model, we also use the LeakyReLU function as the activation function, and the LeakyReLU function is shown in the Equation (7). LeakyReLU is a commonly used activation function in neural networks that enhances the robustness of the network. Unlike the traditional ReLU activation function, LeakyReLU returns a small slope (typically 0.01) on its negative input range instead of simply returning 0. Its main advantage is that it increases the robustness and non-linearity of neural networks while addressing some training issues. The parameter \(\alpha\) is a constant smaller than 1, typically set to 0.01. The purpose of this function is to increase the non-linear characteristics of neural networks and prevent the issue of dead neurons in the output layer. Additionally, LeakyReLU can mitigate the problem of vanishing gradients during training, thereby improving the training performance of the neural network. The SPP model pooling sizes of the original YOLOv4 are 9 and 10, but due to compatibility problems with the DPU operator of KV260, maxpool can only be downloaded from 22-88. So, we modified the pooling size of the SPP model of YOLOv4 to 3, 5 and 7. Changing the pool size of the initial SPP model can result in information loss and a decrease in spatial resolution, which may impact detection performance for certain specific tasks. However, reducing the pool size allows us to decrease computational requirements to some extent, thereby improving the inference speed and efficiency of the model.  
\[LeakyReLU=max(0,x)+\alpha\times min(0,x) \tag{7}\]  
MobileNetv3 continues the linear bottlenecks and inverted residuals of MobileNetv2, introduces \(3\times 3\) depth separable convolution, uses the hswish activation function instead of the swish function to reduce the amount of calculation, and adds the SE attention mechanism module to improve the detection accuracy of the network model.  
After modifying the feature extraction network and activation function, you only need to train according to the normal method of the YOLOv4 model. Experiments show that the parameters of the improved YOLOv4-MobileNetv3 model after lightweight processing are 11.47 M, which is 82.09% less than the original YOLOv4 network model.  
Figure 5: YOLOv4-MobileNetv3 framework.