# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Ablation Experiment  
In order to verify the effect of algorithm model optimization, the feature extraction network, convolution module, channel pruning, and quantization models in the experiment were ablated experiments, and the experimental results are shown in Figure 10. In Figure 10, A represents the original YOLOv4 network model with 64.04 M parameters and an mAP of 84.47%. B represents the model using the MobileNetv3 feature extraction network without deep separable convolution modules, with a parameter size of 39.99 M and an mAP of 83.69%. C represents the model using the MobileNetv3 feature extraction network with deep separable convolution modules, with a parameter size of 11.47 M and an mAP of 81.54%. D represents the model after data enhancement using GridMask and Mosaic algorithms, with 11.47 M parameters and an mAP of 82.24%. E represents the model after applying channel-level pruning, with 9.42 M parameters and an mAP of 83.03%. F represents the model after Vitis AI quantization, with 5.69 M parameters and an mAP of 82.61%.  
From the data in the figure, it is evident that after replacing the feature extraction network and incorporating deep separable convolution operations, the model accuracy decreased by 2.93%, while the number of model parameters was significantly reduced by 52.57 M. This is because lightweight feature extraction networks and deep separable convolution networks, compared to the original network structure, sacrifice some feature representation capabilities, resulting in lower accuracy. However, this reduction in accuracy comes with significant reductions in the number of model parameters, which leads to improved efficiency when dealing with limited computing resources. Following the data augmentation operation, there was a 0.7% improvement in the accuracy of the algorithm, indicating that data augmentation has a positive effect on enhancing model accuracy. Channel pruning reduces the number of parameters in the model by identifying and removing redundant channels in the network. After pruning, the unused channels no longer contribute to the forward and backpropagation processes, resulting in reduced computational and storage requirements. By applying channel pruning and fine tuning, an additional reduction of 2.05 M parameters is achieved, making the model more lightweight. Moreover, through the fine-tuning and retraining process, the model is further optimized and adapted, leading to improved accuracy and an additional 0.79% performance boost. Channel trimming and fine-tuning effectively compress the model size, reduce parameters, and enhance accuracy through retraining and optimization. This strategy is highly effective for optimizing models, striking a balance between reducing complexity and resource usage while maintaining high performance. Finally, after model quantization, the parameter size of the model decreased by 39.6%, while the accuracy decreased by 0.42%. In conclusion, the model improvement scheme presented in this paper reduces the size of the model parameters to 5.69 M at the expense of 1.86% mAP, which is a reduction of 91.11% compared to the original YOLOv4 model. The impact of each improvement scheme is evident, as they synergistically complement each other and ultimately deliver notable results in terms of model parameter count and detection accuracy metrics.